By Andy May
Christian Freuer has translated this post to German here.
In part one we discussed various estimates of climate sensitivity (ECS, TCR, and observation-based values) and what they mean, especially those reported in the latest IPCC report, AR6. In part 2 we discussed the uncertainty in estimating cloud feedback to surface warming, and cloud feedback’s relationship with ECS. In this part we compare the values from various sources to one another.
AR4, AR5, and AR6 define preindustrial as before 1750 or when the CO2 atmospheric concentration was about 280 ppm. This is just after the worst part of the Little Ice Age. AR6 estimates the total anthropogenic forcing from 1750 to 2019 to be 2.72 W/m2, a 19% increase over AR5’s estimate (AR6, p 926). AR6 also changes the estimate of ECS, both ECS estimates are compared to other estimates in Table 1.
In AR6, the IPCC states that:
“… the best estimate of ECS is 3°C, the likely range is 2.5 to 4°C and the very likely range is 2 to 5°C. It is virtually certain that ECS is larger than 1.5°C.”(AR6, p 926)
They are virtually certain that ECS is greater than 1.5°C/2xCO2. Yet, the peer-reviewed literature contains numerous lower estimates of climate sensitivity to CO2, based on observations, as admitted in AR6 on page 1007. Six lower estimates are listed, in bold, in Table 1. The IPCC does not independently estimate ECS, they gather peer-reviewed estimates made by others and use their best judgement to derive a most likely value and a range of possible values. They appear to have ignored many peer-reviewed observation-based lower estimates of climate sensitivity. Many estimates, far too numerous to list here, show possible values below 1.5°C/2xCO2.
One reason they give for their new ECS higher range and estimate is they believe the “feedback parameter increases as temperature increases.” Feedbacks on top of feedbacks. Thus, they have created an endlessly changing model framework for their calculation, making an already untestable hypothesis even more untestable. When building a computer model, it is never a good idea to use the primary target calculation, in this case surface temperature, drive the model structure or the target feedbacks. This is the computer equivalent of circular reasoning.
The background climate state does change and there is no doubt that feedbacks will have a different effect when the climate state changes. However, AR6 focusses on the temperature-dependence of feedbacks without showing how the climate state changes when temperature changes. Javier Vinós has shown that climate state changes are possibly related to changes in solar activity and major ocean internal oscillations. Thus, it is possible that a changing climate state causes the feedback and temperature changes, and not the other way around. AR6 may have confused cause and effect.
ECS is an artificial model construct with little meaning outside the climate model world. An instantaneous or nearly instantaneous CO2 doubling is unlikely to occur, and it would take hundreds, perhaps thousands, of years for the full ECS temperature response to work through the climate system. It is extremely unlikely that other factors affecting climate would stay in equilibrium that long.
To make matters worse, the models used to calculate ECS are not consistent. Some calculations use a full atmosphere-ocean model and some use observed ocean temperatures. Some simple models construct an energy balance based only upon surface temperature, these are called zero-dimensional models, other simple models add additional zones, or complexities. It is widely recognized that ECS is unreal and as a result some have redefined it as “effective climate sensitivity” as previously discussed in part 1. But this is still unreal, untestable, and not scientific, as defined by Karl Popper. Further it only affects humanity 150, or more, years in the future, a meaningless time frame to consider today.
The CO2 climate sensitivity estimates listed in bold in the bottom six rows of Table 1, are not directly comparable to the IPCC model-based estimates, because they are based on real world observations. These six estimates use data collected over periods of less than 100 years and the CO2 increases occurred over time.
Nicola Scafetta offers a more comprehensive look at the AR6 model ECS estimates. Scafetta shows that AR6 ECS calculations from models range from 1.83 to 5.67°C/2xCO2. He found that all the models with an ECS above 3°C/2xCO2 run very hot relative to observations and should be discounted. Scafetta found that the models that had excess warming (over observations) of less than 0.2°C in 50% or more of their grid cells, were those with an ECS less than 2°C/2xCO2. Further, these are the only models that can be considered statistically valid. Scafetta and many other climate researchers have shown that an ECS between one and two °C/2xCO2 fits observations best, higher values are not supported by observations.
As already mentioned, AR6 relies very heavily on the flawed analysis of Sherwood, et al. The AR6 estimate of ECS, shown in Table 1, is like Sherwood’s, which is about 3.2°C (5-95% range 2.3 – 4.7°C). Using the same data as Sherwood, but using a more objective set of criteria, and fixing some errors in Sherwood’s statistical techniques, Nic Lewis lowers Sherwood’s estimate of climate sensitivity to 2.2°C, from 3.2°C, and finds that values below 2°C have a 36% probability, higher than the probability of climate sensitivity exceeding 2.5°C.
TCR (the Transient Climate Response) is the short-term—roughly 70 years—change in temperature due to a sustained 1%/year increase in CO2 to the point where the CO2 concentration doubles. While TCR is still an artificial construct, it plays out in 70 years and can be checked and potentially falsified. It is both more relevant and scientific. In this discussion, we will ignore the unreal and untestable ECS, whether the “E” stands for equilibrium or effective. Table 2 compares various estimates of TCR to our empirical, observation-based estimates of climate sensitivity in the real world.
The IPCC values of TCR in Table 2 are closer to the measured estimates shown in bold, but still too high. AR6 has this to say about their estimate of TCR:
“… the best estimate of Transient Climate Response (TCR) is 1.8°C, the likely range is 1.4 to 2.2°C and the very likely range is 1.2 to 2.4 °C.”(AR6, p 927).
AR6 on estimates based upon the historical record:
“Global energy budget constraints indicate a best estimate (median) value of TCR of 1.9°C … and very likely in the range 1.3°C to 2.7°C (high confidence).”(AR6, p 999)
Their overall assessment is a little smaller than their estimate from the historical record, but higher than the observation-based estimates we cite in Tables 1 and 2. Clearly, they are cherry picking the data they use. To set the lower bound of their “very likely” range above the six or seven observation-based estimates in Tables 1 and 2 is disingenuous.
AR6 do discuss Nic Lewis and Judith Curry’s 2018 paper, which has a lower bound below 1°C/2xCO2, and similar estimates by Ragnhild Skeie and colleagues, and Alexander Otto and colleagues. Christy and McNider’s 2017 estimate of TCR is completely ignored. AR6 dismisses these lower estimates because the studies necessarily assume radiative feedbacks will remain constant as CO2 causes the atmosphere to warm, at least with respect to ECS. The assumption of constant radiative feedback has a smaller effect on observation-based estimates of TCR. This refers to the IPCC speculative positive feedbacks to feedbacks idea introduced in AR6 as discussed above. They have high confidence that the feedbacks will increase as temperature rises, which will cause additional warming, this confidence comes primarily from model studies. Obviously, observation-based studies must assume that the feedbacks are constant over the period studied. AR6 assumes that climate state changes are a result of temperature changes, that is they are a temperature feedback, and ignores the very real possibility that the temperature changes are due to climate state changes.
Positive feedbacks to feedbacks
The IPCC AR6 models do not predict historical SST warming very well. Depending upon the area, sometimes the models overpredict warming and sometimes they underpredict it. It seems their logic is that the models cannot be wrong, so they assume the temperature feedback values must be changing. They try and explain their logic on pages 989 to 997. Their explanation reminds us of this passage from Karl Popper’s book,
“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 thus gave a ‘conventionalist twist’ to the theory; and by this stratagem they destroyed its much-advertised claim to scientific status.”(Popper, 1962, p. 37).
The detailed description (section 188.8.131.52, page 989) of their positive feedback to feedbacks idea, is based upon comparisons of observed ocean warming versus modeled ocean warming. Quite simply they do not match; as their figure 7.14 on page 990 shows. Their “spatial pattern” analysis of modeled SSTs to observed SSTs, is supported by “multiple generations of climate models” and little else. They call upon the poorly understood net cloud feedback set of adjustable model parameters and use them to explain why the models are not properly predicting Pacific SSTs. Richard Seager and his colleagues have this to say about this idea:
“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, et al., 2019
Ross McKitrick, in his comments on the AR6 second order draft (SOD) Chapter 7, notes that the IPCC base their conjectures about “feedbacks on feedbacks” and a higher ECS on their ability to predict tropical climate accurately. Yet, as he and John Christy explain in their 2018 and 2020 papers, every run of every CMIP5 model over-predicts warming in the 200 hPa to 300 hPa (10-12 km) layer in the tropical troposphere and the differences are statistically significant in most cases. When observations are significantly different than the model results, the simplest explanation is that the models are wrong, not that the feedbacks are changing with increasing temperatures.
Summary and Conclusions
AR6 Chapter 7, “The Earth’s energy budget, climate feedbacks, and climate sensitivity,” was the source for most of the material in the first three parts of this series. It exudes a certain desperation, the reader is inundated with the phrases “high confidence,” “virtually certain,” and “very likely” ad nauseam. They are used less to describe and more to persuade.
When the IPCC discovered they were overestimating warming in the eastern Pacific and in the Southern Ocean, they did not conclude the obvious, that their models were wrong. Instead, they created an elaborate scenario, based on “patterns” of ocean surface warming that hypothesized that their CO2-caused warming feedbacks were subject to positive (warming) feedbacks themselves! Using a key model output, in this case surface temperature, to compute a critical feedback, that in turn is used to compute the same output, makes the model unstable and unreliable.
We have previously emphasized the importance of recognizing that climate change is not a global thing, it varies regionally, and particularly by latitude (see figure 3 here). CO2 is a well-mixed gas and has a nearly constant atmospheric concentration around the world and vertically through the atmosphere. As a result, if CO2 were a significant influence on climate, it might cause climate change globally. Presumably, this is why the IPCC focusses on global changes.
AR6 acknowledges that climate changes regionally, yet they do not acknowledge that this is evidence that their models and assumptions are wrong. Natural climate change is local, mainly by latitude, they seem to have decided that their hypothesized feedbacks are changing at different rates, in regional patterns, and call it the “pattern effect.” Isn’t it more logical to just acknowledge that most of climate change is natural, and that is why the models are not reproducing what we observe?
Finally, the IPCC, as well as many worldwide government agencies, are recommending that we curtail fossil fuel burning to limit warming to 1.5°C above what they call the preindustrial period. This period ends in 1750, the end of the coldest century (~1650-~1750) since the last glacial period, at least in the extra-tropical Northern Hemisphere. Human civilization has never seen colder temperatures. Very few people would want to return to the miserable climate of that time. Our modern climate is better and the additional CO2 we enjoy today has greatly improved agricultural productivity.
The IPCC has failed to measure the impact of CO2 and other GHGs on climate or global warming, that is, measure the climate sensitivity to CO2. Many researchers have used measurements to estimate climate sensitivity, but when those estimates are below what the IPCC wants, they simply ignore them.
The AR6 methodology, like the Sherwood, et al. methodology, was subjective in what estimates were included. In fact, AR6 specifically excludes many valid estimates of climate sensitivity, without explaining why, from page 1007 in Chapter 7:
“History has seen a multitude of studies (e.g., Svensmark, 1998; Lindzen et al., 2001; Schwartz, 2007) mostly implying lower ECS than the range assessed as very likely here.”AR6, p 1007
The “multitude” of estimates is simply ignored, without explanation. The explanation given is that much higher estimates based on paleoclimate studies are also ignored, although, the higher estimates were: “… shown to be overestimated due to a lack of accounting for orbital forcing and long-term ice-sheet feedbacks (Schmidt et al., 2017b).”
AR6, stepped away from the past practice of directly calculating ECS and TCR from model output. Instead, they used measurements, such as those by Lewis and Curry, in combination with several complex model-based calculations to constrain the values of ECS and TCR to an expected range. The methodology as explained in AR6 and in Sherwood, et al. was set up so that model-derived estimates swamped the instrument-based estimates, especially at the low end, allowing them to dial in the output they wanted.
In part 4, the final part of this series we examine how modern observations of CO2 and global average temperature are used to compute climate sensitivity and then how the computation is converted into a pseudo-ECS. Once the conversion is done, what does it mean? Look for part 4 tomorrow.
Download the bibliography here.
Including: Lindzen, R., & Choi, Y.-S. (2009, August 26). On the determination of climate feedbacks from ERBE data. Geophysical Research Letters, 36(16), Lindzen, R., & Choi, Y.-S. (2011, August 28). On the Observational Determination of Climate Sensitivity and Implications. Asia-Pacific Journal of Atmospheric Sciences, 47(377)., Idso, S. (1998). CO2-induced global warming: a skeptic’s view of potential climate change. Climate Research, 10(1), 69-82, Newell, R., & Dopplick, T. (1979). Questions Concerning the Possible Influence of Anthropogenic CO2 on Atmospheric Temperature. J. Applied Meterology, 18, 822-825., and (Lewis & Curry, The impact of recent forcing and ocean heat uptake data on estimates of climate sensitivity, 2018), among many others. ↑
AR6, pp. 981 and Figure 7.11 ↑
(Vinós, Climate of the Past, Present and Future, A Scientific Debate, 2022, pp. 184-187) ↑
AR6, page 980. ↑
(Vinós, Climate of the Past, Present and Future, A Scientific Debate, 2022, p. 189) ↑
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 ↑
Bates, J. R. (2016). Estimating climate sensitivity using two-zone energy balance models. Earth and Space Science, 3(5), 207-225. ↑
(Sherwood, et al., 2020). ↑
Popper, K. R. (1962). Conjectures and Refutations, The Growth of Scientific Knowledge. New York: Basic Books. Pages 35-37. ↑
Scafetta, N. (2021). Testing the CMIP6 GCM Simulations versus Surface Temperature Records from 1980–1990 to 2011–2021: High ECS Is Not Supported. Climate, 9(161) ↑
Lewis, N. (2022). Objectively combining climate sensitivity evidence. Climate Dynamics. ↑
Lewis, N., & Curry, J. (2018, April 23). The impact of recent forcing and ocean heat uptake data on estimates of climate sensitivity. Journal of Climate. ↑
Skeie, R. B., Berntsen, T., Aldrin, M., Holden, M., & Myhre, G. (2018). Climate sensitivity estimates – sensitivity to radiative forcing time series and observational data. Earth System Dynamics, 9, 879-894. ↑
Otto, A., Otto, F. B., Church, J., Hegerl, G., Forster, P. M., Gillett, N. P., . . . Stevens, B. (2013, May 19). Energy budget constraints on climate response. Nature Geoscience, 415-416. ↑
Christy, J., & McNider, R. (2017). Satellite Bulk Tropospheric Temperatures as a Metric for Climate Sensitivity. Asia-Pac. J. Atmos. Sci., 53(4). ↑
AR6, p 996 ↑
AR6, p 990. ↑
AR6, p 990. ↑
Seager, R., Cane, M. H., Lee, D.-E., Abernathey, R., & Zhang, H. (2019, June 24). Strengthening tropical Pacific zonal sea surface temperature gradient consistent with rising greenhouse gases. Nature Climate Change, 517-522. ↑
(McKitrick & Christy, 2018) and (McKitrick & Christy, 2020) ↑
(Vinós, Climate of the Past, Present and Future, A Scientific Debate, 2022, pp. 155-161) ↑
AR6, page 990 ↑
AR6, page 990, see AR6 figure 7.14 for a comparison of model results to observations. ↑
IPCC. (2018). Global Warming of 1.5 degrees C. (Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, . . . a. T. Waterfield, Eds.) Geneva: World Meteorological Organization. ↑
Idso, C. (2013). The Positve Externalities of Carbon Dioxide: Estimating the Monetary Benefits of Rising Atmospheric CO2 Concentrations on Global Food production. Center for the study of Carbon Dioxide and Global Change. ↑
AR6, p 1007 ↑
It all starts from the premise that trace gas emission is the cause of at least 100% of the change computed from the average of near surface temperature anomalies.
To do this, it requires completely dismissing the profound changes to the biohydrological environment during the period of record. It’s just too hard to figure out, they say. Set null.
The unnatural continental heat islands, created by humanity, in addition to all other “climate state” background variation simply cannot exist. Anomalous thermometer temperature readings must only be a response to trace gas and trace gas feedbacks. Full stop.
They ought to refer to further Assessment Reports as
Assessment Report Future
Because they’re getting to be Barking Mad
Where’s the test data that increasing CO2 will increase the temperature at the surface? Where’s the test data that CO2 radiation transfer dominates over N2/O2 conduction/convection?
“Everyone knows that CO2 is a greenhouse gas” isn’t an answer. It’s a greenhouse gas in a greenhouse. Where’s the proof that it does the same thing in the unlimited atmosphere?
We don’t need data, silly goose. We have adjustments. Nor do we need a sound theoretical basis. We have models tuned with different kinds of ad-hoc parametrizations. It’s all so absurd that it must be true. Science.
One thing that is not hard to figure out is the change in solar intensity. Ocean heat uptake in the Northern Hemisphere reaches a maximum in May around the time the Indian monsoon sets in to limit further uptake.
This is how May solar intensity has changed and is changing due to orbital precession:
Indian Ocean temperature around India getting already close to hitting the 30C limit that will sustain the monsoon – per attached from Nullschool..
Solar intensity has increased over the last 3,000 years and will continue to rise for the next 6,000 years. The change in solar intensity is an order of magnitude higher than what is being imagined for the heat trapping of CO2.
There is not much known about ocean response times but it is known that some abyssal water does not get back to the surface for up to 2000 years. So water that descended into the Southern Ocean abyss, when JC was in shorts and the SO was close to its peak solar intensity, is just now getting back to the surface.
More on sun,
we know the annual cycle of irradiance varies in the range 330 to 355.0 Wm-2 or so.
And we know the annual range of temperature is something like 3K.
This gives a very low 25 / 3 or about 8 W;m-2/K observed simply over an annual cycle.
super easy rough estimate.
We see clearly the power of the water cycle to dampen change. It is most certainly not amplifying.
Extrapolating, a 25% change of irradiance would only result in about 10K temperature difference max. A 50% reduction only 20K. Astonishing.
So what of a CO2 doubling virtual forcing of 4 watts? I get about 0.5K upper bound.
You forgot the minus sign. The peak surface temperature occurs when the solar intensity at ToA is at a minimum.
Actual response is plus 25W/m^2 to give minus 3K:
Response coefficient = -25//3 W.m-2.K-1
So more solar input results in lower temperature globally.
If CO2 worked like the sun, then the ECS would be negative.
thank you, it’s a good remark.
I would add that the common explanation that “continental rocks” heat up faster than water, and whatnot, as an explanation as to why Earth is warmest in July is a lazy approach…
In actual fact the more important mechanism is that the cloud fraction % appears to be synchronous with solar irradiance, even on annual cycle. The water process response works in such a way as to counter irradiance variation, and effectively so.
It provides a foundation to understand early “faint sun” paradox and conversely the apparently low sensitivity to CO2.
In fact 4Wm-2 appears to be almost nothing for the water process to snuff, such that it may actually be a perturbation to water process itself which is the primary human factor of influence (if one exists).
The correct answer is climate sensitivity is whatever we want it to be to fit our models and show significant warming.
I estimate the ECS to be between 0 and 5. I think that about covers it. It’s just as good an estimate as any other.
Why only a positive range. Surface temperature and solar intensity are negatively correlated globally. Highest intensity is December when the global temperature is at its lowest.
A brave soul to not contemplate a negative ECS.
But are you confident, very confident, extremely confident or so confident it hurts, in your estimate?
“”””” One reason they give for their new ECS higher range and estimate is they believe the “feedback parameter increases as temperature increases.” Feedbacks on top of feedbacks. “””””
Circular logic if I ever heard it. Simply can’t be that “we” are wrong, it must be something else, on and on.
This also allows a “God, we are doomed mindset”. I just can’t get the image of the guy with sandwich placards “THE END IS NEAR” out of my mind!
Here is the same idea updated.
How about this one?
There goes the Runaway Global Warming Theory again. 😉
Climate sensitivity is a massively confused concept. If it is an “all else being equal” abstraction then it tells us nothing about what will actually happen, when all else occurs. If it is a prediction it is absurd because CO2 level alone does not determine temperature. Either way it is useless, but enormous resources are devoted to analyzing it and acting on it. An expensive confusion indeed.
they’re trying to count the angels on the head of a pin
Speaking of “all else being equal”….
The Modtran base case for the following was clear sky, Mid-latitude winter, 400 ppm CO2…then changed to 800 ppm CO2 and rerun at offset temperature cases at fixed relative humidity until the Q leaving was the same as the base case. Result is .82 C of Surface warming.
The second run started with clear sky, Tropical, 400 ppm CO2….then changed to 800ppm CO2. Again running surface temp offset until Q matched the base case at fixed relative humidity…Result is 1.21 C surface warming.
Here is the second described case with 2x CO2 resulting in 1.21 C increase. Fixed RH takes into account water vapor “feedback”. Modtran uses multi-layer IR absorptivities, so radiatively very good, and is pretty good with its parameterization of cloud effects or it would not give as good of answers as it does.
So here we have fairly representative but clear sky cases (more warming than cloud cases) that show 2x CO2 of 1.21 and below. How can IPCC come up with number of over 3 when university students in their METEO class can calculate numbers 1/2 to 1/3 of theirs ?
The problem with radiation models is assuming “fixed relative humidity”. The upper atmosphere relative humidity will drop. This completely changes the result. Even the low numbers you quote are too high.
TCS at less than one degree C is not scary enough for the Greens. Therefore, it must be rejected.
There is a way to ‘back of envelope’ estimate ECS using the Bode feedback curve.
IPCC says ECS ~ 3 based on models that run hot. That translates to Bode f of ~0.65. IPCC says water vapor feedback (WVF) about doubles the no feedback case ~1.2. 2.4 WVF translates to Bode f 0.5.The remaining f 0.15 must be clouds, since IPCC says all else nets to about 0.
Now we have known since Dessler 2010 that the actual cloud feedback is about zero or slightly negative. And we now know from ARGO that there is about twice as much ocean rainfall as modeled, so water vapor feedback is about half of modeled. That means f ~0.25, giving ECS ~1.75. Right in the zone of the EBM estimates.
What was the CO2 concentration in the atmosphere during the Roman and Medaeval Warm periods? How much has average global temperature risen between then and now compared to the increase in CO2 concentration? From what I know the answer would suggest that ECS is minimal if not zero.
“They are virtually certain that ECS is greater than 1.5°C/2xCO2. Yet, the peer-reviewed literature contains numerous lower estimates of climate sensitivity to CO2, based on observations, as admitted in AR6 on page 1007.”
Thank you for that tidbit. Now I don’t have to slog through the entire IPCC AR6 monstrosity to know that “they” ignore inconvenient truths.
Regarding “AR4, AR5, and AR6 define preindustrial as before 1750”: I clicked the red “26” in “what they call the preindustrial period. This period ends in 1750”, and got looped back to this article. I did not see a footnote 26.
Meanwhile, I saw in AR6 “Specific global warming levels, such as 1.5°C, 2°C, 3°C or 4°C, are defined as changes in global surface temperature relative to the years 1850–1900 as the baseline”, in https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_TS.pdf , although this also states 1750 as the baseline year for radiative forcings. Atmospheric CO2 did not increase much from 1750 to 1900; most of the warming from 1750 to the 1850-1900 temperature baseline was from recovery from the Little Ice Age.
Don, Sorry some of the footnotes didn’t make it through. I added the missing ones. 26 is there now.
“Concerning the Pre-industrial”
Confusing to be sure, here is how AR6 explains it on page 192 in chapter 1.
So, they define preindustrial as before 1750, but use temperatures since 1850, as if there were no difference.
Regarding “We have previously emphasized the importance of recognizing that climate change is not a global thing, it varies regionally, and particularly by latitude (see figure 3 here). CO2 is a well-mixed gas and has a nearly constant atmospheric concentration around the world and vertically through the atmosphere. As a result, if CO2 were a significant influence on climate, it might cause climate change globally.”: The Arctic and near-Arctic has a strong regional positive feedback from surface albedo varying with temperature. The Antarctic intermittently had a similar feedback during the past few 10s of millions of years, also part of the Antarctic’s cooling between 52 million years ago and now was from Antarctica moving into the Antarctic and getting covered by snow and then ice as a result.
Right, Antarctica formed when the Southern Ocean opened up and the Arctic cap formed when Panama formed and cut off tropical east-west flow between the Atlantic and Pacific oceans increasing Atlantic meridional transport of energy in the Northern Hemisphere. The attached is from page 200 in Javier’s book:
(6) (PDF) Climate of the Past, Present and Future. A scientific debate, 2nd ed. (researchgate.net)
This is only half the story. It also varies seasonally as would be expected with the peak solar intensity moving northward.
The place AND time of most global warming is the Greenland plateau in winter!
This little gem suggests humanity will witness the full impact of CO2 increasing in thousands of years. And yet there is full denial that the much larger changes in solar intensity due to orbital precession does anything.
The May solar intensity at 40N has increased from 437W/m^2 to 444W/m^2 in 2000 years. Any lagged response of the order of 2000 years in the oceans will be just showing that change now. Not only in response to the 9W/m^2 increase already in the last 2000 years but the intensity peaks at 463W/m^2 in 6,000 years – so another 19W/m^2 still to come.
So on one hand CO2 is going to have full impact on temperature based on an imaginary heat trapping in thousands of years but an order of magnitude higher change in real energy over thousand of years is not having, or going to have, any impact on the temperature. This is climate phiisics at its best. The true magic of heat trapping CO2.
What did the May solar intensity at 40S do in the last 2000 years?
What about November at both 40N and 40S over the same time period?
Regarding “they believe the “feedback parameter increases as temperature increases.””
“2 AR6, pp. 981 and Figure 7.11 ↑“:
The link loops back to this article. I found pp. 981 and Figure 7.11 at:
This does indeed say that the feedback parameter gets less negative (climate sensitivity increases) as temperature increases. I disagree; the surface albedo feedback is most positive when a temperature change causes the most change in reflection of sunlight by ice and snow cover. When there was variability of ice sheet coverage on Eurasia and North America and intermittent snow coverage south of these ice sheets, the surface albedo feedback had to be stronger than it is now. Global temperature was certainly a lot more volatile during glacial periods than during interglacials in the past 450,000 years. There may even have been times when, depending on snow & ice coverage and the state of the Milankovitch cycles, there was a regional instability of climate in part(s) of the Northern Hemisphere and a change of snow and/or ice sheet coverage temporarily went into runaway. And as the world gets warmer than it is now, I expect the zone of variable snow and ice cover to shift northward and receive less sunlight and shrink in area (with the area shrinkage temporarily reversing if Greenland’s ice sheet melts down). This would cause the surface albedo feedback to decrease from the world getting warmer than it is now.
As for how great (or small) climate sensitivity appears to me: Global temperature change since 1850-1900 indicates climate sensitivity in the lower climate sensitivity half of the CMIP3 and CDMIP5 models, and probably in line or nearly in line with figures indicated by studies by Lewis & Curry and by Nic Lewis (TCR generally around 1.2 to 1.5, ECS generally around 1.5 to 2 deg. C per 2xCO2).
All the little arrows loop back to the article. I’m not even sure why they are there.
From the article: “They are virtually certain that ECS is greater than 1.5°C/2xCO2. Yet, the peer-reviewed literature contains numerous lower estimates of climate sensitivity to CO2, based on observations, as admitted in AR6 on page 1007. Six lower estimates are listed, in bold, in Table 1.”
So, the science isn’t settled.
In the future, when you hear someone tell you they know how much warmth a certain amount of CO2 will trap, you can refer them to this article and tell them they don’t know what they are talking about and are just guessing.
This has been looked at for a very long time and still nobody knows the answer.
We shouldn’t be destroying our economies in an attempt to control CO2, considering the uncertainties. But that’s what our glorious leadership is doing, all based on speculation, unsubstantiated assumptions, and assertions, about CO2 and the Earth’s atmosphere.
The IPCC itself can be included in those “many other climate researchers” if you allow some latitude when putting numbers on the exact thresholds between “good” and “bad” fits.
A copy of Figure 7.19, which can be found on page 1009 of the AR6 WG-I report, is attached below.
ECS > 5°C (per CO2 doubling) models are often outside the “likely range” derived from observations.
Ironically the clearest example for this is “post 1975”, which uses (relatively …) dense global instrumental datasets, including global satellite coverage from 1979, instead of sparse (in both space and time resolutions) proxies.
Note also just how “too cold” the ECS > 5.0 model hindcast is for the LGM (around 18-20 kya), which reinforces the “the CMIP6 models are just too damn sensitive” conjecture.
Lewis 2023 gives 2.16 C.
The Water Vapor Factor
Water vapor is a transparent gas that, molecule for molecule, is at least as effective at absorb/emit of earth-temperature infrared radiation (IR) as carbon dioxide. From Jan 1988 thru Dec 2021 NASA/RSS accurately measured and reported monthly the global average water vapor as Total Precipitable Water (TPW). In Jan 2022 they stopped reporting new global average TPW measurements, a year later deleted the website completely and sometime after that replaced it at http://data.remss.com/vapor/monthly_1deg/tpw_v07r01_198801_202112.time_series.txt. (If they delete the website again it can still be recovered via the wayback machine). At ground level, during the period of measurement, water vapor molecules increased about 7 times faster than CO2 molecules.
Further analysis shows that the determination by molecule count that increased CO2 influence on the climate has been only 1/7 as much as the increased water vapor influence is still high. Radiation from water vapor molecules can be in any direction but, because of the steep decline with altitude of the population gradient of water vapor molecules, the distance traveled by a photon before it encounters another water vapor molecule is greater towards space than towards earth so the prevailing direction of IR flux is towards space. This is shown on a Top of Atmosphere (TOA) graph of radiation flux vs wavenumber (wavenumber is the number of wavelengths in a centimeter) by the jagged line below about wavenumber 600. Because of the characteristic absorb/emit signature of every gas no other gas can significantly absorb or emit radiation in the wavenumber range occupied by water vapor. The line is jagged because radiation is from water vapor molecules at different temperatures/altitudes reaches TOA/space.
At about 2 km and for a few km higher, the outward directed radiation from water vapor can make it all the way to space. In this altitude range energy absorbed by CO2 and other IR active molecules is redirected with respect to wave number via gaseous conduction to replenish the substantial energy radiated to space by water vapor molecules. This eliminates any warming from increased CO2 (or any other IR active gas that does not condense at earth temperatures) in the troposphere.
At the tropopause (about 8 to 16 km depending mostly on latitude; higher at the equator) and above, water vapor molecules are greatly diminished because of low temperature so radiation to space is mostly from CO2 and other IR active molecules that do not condense in the atmosphere. Increased CO2 in the extremely thin air there actually counters warming.
The end result is that CO2 does not cause significant climate change and the Green New Deal will have no significant effect on climate.