Posted on April 1, 2019 by curryja |
by Judith Curry
Are values of equilibrium climate sensitivity > 4.5 C plausible?
For background, see these previous posts on climate sensitivity [link]
Here are some possibilistic arguments related to climate sensitivity. I don’t think the ECS example is the best one to illustrate these ideas [see previous post], and I probably won’t include this example in anything I try to publish on this topic (my draft paper is getting too long anyways). But possibilistic thinking does point you in some different directions when pondering the upper bound of plausible ECS values.
5. Climate sensitivity
Equilibrium climate sensitivity (ECS) is defined as the amount of temperature change in response to a doubling of atmospheric CO2 concentrations, after the climate system has reached equilibrium. The issue with regards to ECS is not scenario discovery; rather, the challenge is to clarify the upper bounds of possible and plausible worst cases.
The IPCC assessments of ECS have focused on a ‘likely’ (> 66% probability) range, which has mostly been unchanged since Charney et al. (1979), to be between 1.5 and 4.5 oC. The IPCC AR4 (2007) did not provide any insight into a worst-case value of ECS, stating that values substantially higher than 4.5 oC cannot be excluded, with tail values in Figure 9.20 exceeding 10 oC. The IPCC AR5 (2013) more clearly defined the upper range, with a 10% probability of exceeding 6 oC.
Since the IPCC AR5, there has been considerable debate as to whether ECS is on the lower end of the likely range (e.g., < 3 oC) or the higher end of the likely range (for a summary, see Lewis and Curry, 2018). The analysis here bypasses that particular debate and focuses on the upper extreme values of ECS.
High-end values of ECS are of considerable interest to economists. Weitzman (2009) argued that probability density function (PDF) tails of the equilibrium climate sensitivity, fattened by structural uncertainty using a Bayesian framework, can have a large effect on the cost-benefit analysis. Proceeding in the Bayesian paradigm, Weitzman fitted a Pareto distribution to the AR4 ECS values, resulting in a fat tail that produced a probability of 0.05 of ECS exceeding 11 oC, and a 0.01% probability of exceeding 20 oC.
The range of ECS values derived from global climate models (CMIP5) that were cited by the IPCC AR5 is between 2.1 and 4.7 oC. To better constrain the values of ECS based on observational information available at the time of the AR5, Lewis and Grunwald (2018) combined instrumental period evidence with paleoclimate proxy evidence using objective Bayesian and frequentist likelihood-ratio methods. They identified a 5–95% range for ECS of 1.1–4.05 oC. Using the same analysis methods, Lewis and Curry (2018) updated the analysis for the instrumental period by extending the period and using revised estimates of forcing to determine a 5-95% range of 1.05 – 2.7 oC. The observationally-based values should be regarded as estimates of effective climate sensitivity, as they reflect feedbacks over too short a period for equilibrium to be reached.
Values of climate sensitivity exceeding 4.5 oC derived from observational analyses are arguably associated with deficiencies in the diagnostics or analysis approach (e.g. Annan and Hargreaves, 2006; Lewis and Curry, 2015). In particular, use of a non-informative prior (e.g. Jeffreys prior), or a frequentist likelihood-ratio method, narrows the upper tail considerably. However, as summarized by Frame et al. (2006), there is no observational constraint on the upper bound of ECS.
The challenges of identifying an upper bound for ECS are summarized by Stevens et al. (2016) and Knutti et al. (2017). Stevens et al. (2016) describes a systematic approach for refuting physical storylines for extreme values. Stevens et al.’s physical storyline for a very high ECS (>4.5 oC) is comprised of three conditions: (i) the aerosol cooling influence in recent decades would have to have been strong enough to offset most of the effect of rising greenhouse gases; (ii) tropical sea-surface temperatures at the time of the last glacial maximum would have to have been much cooler than at present; and (iii) cloud feedbacks from warming would have to be strong and positive.
An interesting challenge to identifying the plausible upper bound for ECS has been presented by a newly developed climate model, the DOE E3SM (Golaz et al. 2019), which includes numerous technical and scientific advances. The model’s value of ECS has been determined to be 5.3 oC, higher than any of the CMIP5 model values and outside the IPCC AR5 likely range. This high value of ECS is attributable to very strong shortwave cloud feedback. The DOE E3SM model’s value of shortwave cloud feedback is larger than all CMIP5 models; however, shortwave cloud feedback is weakly constrained by observations and physical understanding. A stronger argument for placing the DOE E3SM value of climate sensitivity in the ‘borderline impossible’ category is Figure 23 in Golaz et al. (2019), which shows that the global mean surface temperature simulated by the model during the period 1960-2000 is as much as 0.5 oC lower than observed, and that since the mid-1990s the simulated temperature rises far faster than the observed temperature. This case illustrates the challenge of refuting scenarios associated with a complex storyline or model, which was noted by Stevens et al. (2016).
An additional issue regarding climate model derived values of ECS was raised by recent paper by Mauritsen et al. (2019). An intermediate version of the MPI-ESM1.2 global climate model produced an ECS value of ~ 7 oC, caused by the parameterization of low-level clouds in the tropics. Since this model version produced substantially more warming than observed in the historical period, this model version was rejected and model cloud parameters were adjusted to target a value of ECS closer to 3 oC, resulting in a final ECS value of 2.77 oC. The strategy employed by Mauritsen et al. (2019) raises the issue as to what extent climate model-derived ECS values are truly emergent, rather than a result of tuning that explicitly or implicitly considers the value of ECS and the match of the model simulations with the historical temperature record.
Was Mauritsen et al. (2019) justified in rejecting the model version with an ECS value of ~ 7 oC? Is the MPI-ESM1.2 value of ECS of 5.3 oC plausible? Observationally-derived values of ECS (e.g. Lewis and Curry, 2018) are inadequate for defining the upper bounds of ECS. There are two types of constraints that in principle can be used: emergent constraints and the Transient Climate Response.
Emergent constraints in principle can help narrow uncertainties in climate model sensitivity through empirical relationships that relate a model’s response to observable metrics. These analyses have mostly focused on cloud processes. The credibility of an emergent constraint relies upon the strength of the statistical relationship, a clear understanding of the mechanisms underlying the relationship, and the accuracy of observations. Further, the most robust emergent constraints are for model parameters that are driven by a single physical process (e.g. Winsberg, 2018). Investigations of integral constraints related to cloud processes have mostly concluded that the climate models with ECS values on the high end of the IPCC AR5 likely range show best agreement with the integral constraints (e.g. Caldwell et al., 2018). However, Caldwell et al. (2018) and Winsberg (2018) caution that additional processes influencing the metric and other biases in the model may affect the analysis. While the robustness and utility of these emergent constraints continues to be investigated and debated, this technique is not very helpful in identifying a plausible upper bound or in rejecting high values such as obtained by Golaz et al. (2019) and Mauritzen et al. (2019).
The Transient Climate Response (TCR) in principle can be of greater utility in providing an observational constraint on climate sensitivity. TCR is the amount of warming that might occur at the time when CO2 doubles, having increased gradually by 1% each year over a period of 70 years. Relative to the ECS, observationally-determined values of TCR avoid the problems of uncertainties in ocean heat uptake and the fuzzy boundary in defining equilibrium owing to a range of timescales for the various feedback processes. Further, an upper limit to TCR can in principle be determined from observational analyses.
TCR values cited by the IPCC AR5 have a likely (>66%) upper bound of 2.5 oC and < 5% probability of exceeding 3 oC. Knutti et al. (2017; Figure 1) show several relatively recent TCR distributions whose 90 percentile value exceeds 3 oC. Observationally-derived values of TCR determined by Lewis and Curry (2018) identified the 5-95% range to be 1.0–1.9 K. As discussed by Lewis and Curry (2015) and Lewis and Grunwald (2017), use of a non-informative prior or a frequentist likelihood-ratio method narrows the upper tail considerably. While the methodological details of determining values of TCR from observations continue to be debated, in principle the upper bound of TCR can be constrained by historical observations.
How does a constraint on the upper bound of TCR help constrain the high-end values of ECS? A TCR value of 2.93 oC was determined by Golaz et al. (2019) for the MPI-ESM1.2 model, which is well above the 95% value determined by Lewis and Curry (2018), and also above the IPCC AR5 likely range. Table 9.5 of the IPCC AR5 lists the ECS and TCR values of each of the CMIP5 models. If a TCR value of 2 oC is used as the maximum plausible value of TCR based on the Lewis and Curry (2018) analysis, then it seems reasonable to classify climate model-derived values of ECS associated with TCR values ≤ 2.0 oC as verified possibilities.
In light of the cited analyses of ECS (which are not exhaustive), consider the following classification of values of equilibrium climate sensitivity relative to the π-based classifications provided in the possibilistic post, which provides the expert judgment of one analyst (moi). Note that overlapping values in the different classifications arise from different scenario generation methods associated with different necessity-judgment rationales:
- ECS < 0: impossible
- 0 > ECS < 1 oC: implies negative feedback (unverified possibility)
- 1.0 ≤ ECS ≤ 1.2 oC: no feedback climate sensitivity (strongly verified, based on theoretical analysis and empirical observations).
- 1.05 ≤ ECS ≤ 2.7 oC: empirically-derived values based on energy balance models from the instrumental period with verified statistical and uncertainty analysis methods (Lewis and Curry, 2018) (corroborated possibilities)
- 1.15 ≤ ECS ≤ 4.05 oC: empirically-derived values including paleoclimate estimates (Lewis and Grunwald, 2018) (verified possibilities)
- 2.1 ≤ ECS ≤ 4.1 oC: derived from climate model simulations whose values of TCR do not exceed 2.0 oC. (Table 9.5, IPCC AR5) (verified possibilities)
- 4.5 < ECS ≤ 6 oC: borderline impossible
- ECS > 6oC: impossible
In evaluating the justification of the high-end values of ECS, it is useful to employ the logic of partial positions for an ordered scale of events. It is rational to believe with high confidence a partial position that equilibrium climate sensitivity is at least 1 oC and between 1 and 2.7 oC, which encompasses the strongly verified and corroborated possibilities. This partial position with a high degree of justification is relatively immune to falsification. It is also rational to provisionally extend one’s position to believe values of equilibrium climate sensitivity up to 4.1 oC – the range simulated by climate models whose TCR values do not exceed 2.0 oC — although these values are vulnerable to improvements to climate models and our observational estimates of TCR, whereby portions of this extended position may prove to be false. High degree of justification ensures that a partial position is highly immune to falsification and can be flexibly extended in many different ways when constructing a complete position.
The conceivable worst case for ECS is arguably ill-defined; there is no obvious way to positively infer this, and such inferences are hampered by timescale fuzziness between equilibrium climate sensitivity and the larger earth system sensitivity. However, one can refute estimates of extreme values of ECS from fat-tailed distributions > 10 oC (e.g. Weitzman, 2009) as arguably impossible – these reflect the statistical manufacture of extreme values that are unjustified by either observations or theoretical understanding, and extend well beyond any conceivable uncertainty or possible ignorance about the subject.
The possible worst case for ECS is judged here to be 6.0 oC, although this boundary is weakly justified. The only evidence for very high values of ECS comes from climate model simulations with very strong positive cloud feedback (e.g. Mauritzen et al. 2019; Golaz et al. 2019) and statistical analyses that use informative priors. Further examination of the CMIP6 models is needed to assess the causes, outcomes and plausibility of parameters and feedbacks in these models with very high values of ECS before rejecting them as impossible.
With regards to the plausible worst case (lower bound of borderline impossible values) of ECS, consideration was given to the upper bound of verified possibilities (4.1 oC) and also the time-honored value of 4.5 oC as the upper bound as the ‘likely’ range for ECS. Consideration of the model’s value of TCR in comparison to observationally-derived values of TCR seems to be a useful constraint for assessing the plausibility of a model’s ECS value. However, further investigation is needed to understand the methodological differences in the varying estimates of TCR and the causes of varying relations between TCR and ECS values among different models. This seems to be a more fruitful way forward than the emergent constraints approach.
Given that 4.5 oC was specified by the IPCC AR5 as the upper bound of the likely range (> 66% probability), the judgment here that specifies 4.5 oC as the maximum plausible value of ECS will undoubtedly be controversial. Other analysts may make different judgments and draw a different conclusion on this. Consideration of different rationales for making judgments on the maximum plausible value of ECS would illuminate the underlying issues and rationales for judgments.
Because of the central role that ECS plays in Integrated Assessment Models used to determine the social cost of carbon that is largely driven by tail values of ECS, the issue of clarifying the plausible and possible values of ECS is not without consequence.
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I do not agree that 0 degrees or less is impossible.
CO2 has significantly over saturated the available near earth radiation. Combined with water vapor, this means more CO2 should be able to do pretty close to diddly squat. But, as it mixes through the entire atmosphere, increasing CO2 can effectively increase the amount of radiation the atmosphere sends to space. So, it can have a cooling effect.
“”…extreme values that are unjustified by either observations or theoretical understanding, and extend well beyond any conceivable uncertainty or possible ignorance about the subject.”‘
That kind of sums up the “whole thimbleful of fact lost in a bushel of propaganda” concept.
I have kinda lost interest in models which simply dismiss substantve and variable effects in their initial assumptions, blame it all on CO2 and then overpredict reality by 100’s of %. I would like to onow one simple figure from climate modellers. What is the actual sensitivity of the ov ceanic feedback from evaporation, condesation and cloyd albedo, per degree SST anomaly. With that we can immediately see what the relative natural control of 1.6W/m^2 is, if it is correct and not a lot less, as it appears from actual data.
I suggest Thor’s hammer has some similarities, and this control through the oceans has happily brought us through many ice ages, in fact mainataining euqilibirum across a few degress within a 10 degree max band at the polse. Why are we even talking about this when the records of the the current interglacial show continual variation of a degrees every 250 years or so, up then down, never stable, and a decline of the long term trend to the interglacial floor level that the last down took us through. We are lucky to have got back inside the interglacial range, just, and have another 1.5 degrees to go to reach the interglacial median.
Warming is not our problem. Change is natural and we are, or were, due a rise. Neither is any such change catastrophic, even if it is down, the real problem, its 0.5 degrees per century, so several lifetimes of adapting and then relocate, 100 metres downhill?. This is all simple fact anyone can check for themselves, so why are we talking about something else that is quite unreal and has no supporting evidence. Here it is in case you didn’t know what happened for the 7,000 years before 1,000 AD. https://www.dropbox.com/s/vvyej6raim9hjic/Facts%20About%20Climate%20History.pdf?dl=0
I’m an idiot. Got into other ares, Here is the key request from climate muddles. I would like two or three parametisations quantified in W/m^2, at current interglacial conditions will do, to compare their feedback with the supposed effect of AGW of 1.6W/M^2.
I seek the sum of the effects of evaporation, condensation and resulting albedo in W/m^2 caused by 1 degree change in SST. Nice and simple. Never seen a figure, yet this is the single effect stablilising the SST against all change by modifying solar insolation and standing between us and Venus. I suspect the truth is inconvenient for disaster scenarios, and why we stayed inside 10 degrees range or so since we had oceans, etc.
Climate sensitivity appears to be ripe for confounding factors. The “what if’s” of inadequately understood complicated systems. The evidence for one will likely be the oceans. As they continue disgorging heat likely put there during the last cold stadial period, the planet continues to green, which increases insect populations, and increases carbon from decay of all that green stuff, and oceans disgorge CO2. Under the warming atmosphere compliments of the disgorging oceans, greening will increase every year. Until that is the oceans are affected by orbital mechanics that starts putting heat back into the oceans as we fall back down to cold. CO2 is not the leader, it is the signal of some other process. One that has been going on for at least the past 800,000 years.
> 5-95% range of 1.05 – 2.7 °C
The problem with reasoned and supported conclusions like this is that the result is not scary. The other “problem” is that as it relies on observation, as time goes by it gains greater weight in the record.
Question. RCP 8.5 is no longer remotely possible short of a cosmic event. Why is it not dropped from ensemble averages?
I doubt very much that ECS is a constant. It varies based on temperature, humidity, altitude (pressure) and who knows what. Hence, anyone looking for some magic ECS will forever be disappointed.
Now, it may be possible to compute an average ECS similar to the average surface temperature. However, that would probably be just about as useful.
This is just another case of blind folks trying to tell us what the elephant looks like.
An average ECS would be like saying the average American has 1 boob, 1 ball, and 2 1/2 kids.
Joel O’Bryan
“All just delusional “make work” that does nothing to bring any understanding to the climate.”
That pays the witch doctors very well.
I had a critical, but polite post on this subject removed by mods in the first Curry posting. I’m glad to see a bit more critical to dismissive comments abound this time. We can’t have sacred cows in this battle.
I have considerable respect for Curry. She’s been in the lion’s den of the climateers when she could have been a well paid, decorated, and highly respected cheerleader for the totes.But ceteris paribus nonsense on ECS in a system with so many confounding agents acting or waiting to act counter to ‘forcings’ (see Le Chatelier’s Principle even in Wiki) can only give encouragement to hangers on in a falsified meme.
Willis, Lintzen and a few others have waxed strongly and convincingly on the existential necessity of the presence of negative feedbacks for there even to be a world that we and our millions of other specie fellow citizens of this planet can be alive on. What are the constraints on temperature range (nevermind ECS)? The two billion years of the chain of life is a powerful signal for the stability of earth climate. Mass extinctions appear to be from extraterrestrial catastrophic bombardment of large bolides that jerked climate to extremes with mechanical shock, horrific tsunamis, rain of hot glass, and longlasting dust that blocked the sun. We (collectively) barely survived, but, thanks to powerful negative feedbacks, the earth system righted itself and survivors re-spread around the earth. What was the ECS? Nothing that mattered.
At least one negative feedback is obvious. Increasing temperature increases water evaporation (especially over the oceans where wind and wave action makes mass and heat equilibration rapid). More water vapor means more clouds. More clouds mean more diffraction of incident sunlight to space.
The climatistas have to reduce cloud W/m2 effect in the models because if they had it at a more realistic value their models would no longer fit the 20thC training period. Likewise they can’t model clouds properly because if they did this negative feedback would overwhelm the effect of CO2 and model-derived ECS would be low. The ensemble climate models are notorious for poorly modelling clouds.
All this fits with the view that solar modulation of cloud cover and the ~60 year ocean cycle caused most warming last century, and CO2 only contributed a small amount. I am sure if the modellers included those two significant variables correctly (and not the laughable 0.05 W/m2 for solar variability) then their models would forecast much much better.
But if they did that the ECS value would be well under 1 K/doubling, global warming would be proven harmless and most climate programs would be defunded.
“5. Climate sensitivity
Equilibrium climate sensitivity (ECS) is defined as the amount of temperature change in response to a doubling of atmospheric CO2 concentrations, after the climate system has reached equilibrium. The issue with regards to ECS is not scenario discovery; rather, the challenge is to clarify the upper bounds of possible and plausible worst cases.
The IPCC assessments of ECS have focused on a ‘likely’ (> 66% probability) range, which has mostly been unchanged since Charney et al. (1979), to be between 1.5 and 4.5 oC. The IPCC AR4 (2007) did not provide any insight into a worst-case value of ECS, stating that values substantially higher than 4.5 oC cannot be excluded, with tail values in Figure 9.20 exceeding 10 oC. The IPCC AR5 (2013) more clearly defined the upper range, with a 10% probability of exceeding 6 oC.”
——————————
The paragraphs selected above are very interesting, I think.
The first paragraph consist with the consideration of the ECS definition and the relation of its actual consideration.
Definition of ECS as it stands, requires, needs and depends on a confirmation and support by the scenario discoveries, aka experiments, or as in the actual case the models…
as it tightly relates in substance of it’s claim and existence as a concept to the origin of it, to the scenario discoveries of the “experiment”.
None of proper models offers such support or confirmation, as far as I can tell… for a ECS > 3C.
In consideration of such, the result from the models have ECS at < then 3C…even for the few that get to do a doubling of CO2….
And when it comes to the second paragraph, the question to put forward is;
The ECS as per the definition, does it really originates or stands as properly being proposed as a such concept, as considered these days, by the Charney et al. (1979), or is that linkage silly and over claimed and over pushed?
I am not really sure about this one, that is why am asking…any info in this one very appreciated.
ECS is not simply CS, is a special case of CS considered…in the "science" of AGW.
cheers
I’m sorry, but here is an excerpt from the post:
“Emergent constraints in principle can help narrow uncertainties in climate model sensitivity through empirical relationships that relate a model’s response to observable metrics.”
===============
I’ve tried reading it forwards and backwards, can anyone tell me in simple terms what it says ?
u.k.(us)
Maybe it means that the most valid and proper models to consider in relation to ECS are the ones that may be considered “realistic”, the ones that do go through in realistic step with the CO2 concentration path scenarios as per merit of comparison with the reality in context of CO2 concentration path, or the models who do a very close enough CO2 concentration trend as in consideration of the CO2 concentration trend in reality.
And there is no many such as…and as far as I can tell none do reach a doubling, and none of these do an ECS>3C.
Maybe…
cheers
Complex (highly evolved) life requires a relatively stable climate for tens of millions of years (at least) to exist.
Therefore, this planet has a self regulating climate system which keeps it within the temp range where said life continues and saying that adding a few ppm co2 will change that is some kind of loopy joke.
I mean how obvious does it need to be?
All talk of feedback mechanisms is purely academic.
Pretty certain we’re at about 0.55°C before the cloud cover starts to drastically increase with increase average sustained minimum winds.
That was the whole problem with the “global weirding” theory: that it was going to primarily increase the maximums of things but when you put more energy into a shuddering and halting system it only increases the lower end activity.
The “climate” is perfectly capable of dealing with a 10% increase in solar input without turning the planet uninhabitable.
Or we wouldn’t be here at all.
errm; the claim is that doubling CO2 (from 280 to 560 ppm) adds about 3 watts/sqM of warming and that this, with feedbacks, causes what? 3C, 4.5C maybe 6C warming. Lets go with the lowest 3C then ECS is 1watt/sqM/C. But the total energy contribution from CO2 is claimed to be 30 watts/sqM. Sure absolute sensitivity is not the same as incremental sensitivity but 30 watts/sqM is still a pretty small fraction of the total green house gas contribution of about 160 watts/sqM so the non linear effect should be relatively small. So the total contribution of CO2 should be about 30K. That means without CO2 earths temperature would be not 288K but 258K. But as has also been repeatedly pointed out, without green house gases Earth’s temperature would be about 255K. So all of water vapour and clouds contributes 3K of warming. If its so small it can hardly exert much feedback so where does the massive positive feeedback come from?
The climate sensitivity of CO2 is zero as a worst case. It is all a matter of science. The radiant greenhouse effect has not been observed in a real greenhouse, in the Earth’s atmosphere, or anywhere else in the solar system for that matter. The radiant greenhouse effect is science fiction so hence everything based on the radiant greenhouse effect is science fiction including the AGW conjecture.
“The radiant greenhouse effect has not been observed in a real greenhouse, in the Earth’s atmosphere, or anywhere else in the solar system for that matter.”
Tell that to your miitary.
Modtran and Hitran (LbL radiative models) are used to keep you safe and were developed as such.
https://en.wikipedia.org/wiki/HITRAN
https://en.wikipedia.org/wiki/MODTRAN
“HITRAN – HITRAN (an acronym for High Resolution Transmission) is a compilation of spectroscopic parameters that a variety of computer codes use to predict and simulate the transmission and emission of light in gaseous media including the atmosphere, laboratory cells, etc. The original version was compiled by the Air Force Cambridge Research Laboratories (1960s). HITRAN is maintained and developed at the Harvard-Smithsonian Center for Astrophysics, Cambridge MA, USA.
HITRAN is the worldwide standard for calculating or simulating atmospheric molecular transmission and radiance from the microwave through ultraviolet region of the spectrum.[citation needed] The current version contains 49 molecular species along with their most significant isotopologues. These data are archived as a multitude of high-resolution line transitions, each containing many spectral parameters required for high-resolution simulations. In addition there are 320 molecular species collected as cross-section data. These latter include anthropogenic constituents in the atmosphere such as the chlorofluorocarbons.”
Oh BTW:
Neither is the trapping of heat in your garden greenhouse anywhere near an analogue for what happens in the atmosphere.
Try educating yourself…..
https://scienceofdoom.com/roadmap/atmospheric-radiation-and-the-greenhouse-effect/
Uhmmmm. Errrrr. We just don’t know and don’t have the capability to know?
“ECS < 0: impossible" – as others have commented above, I disagree with Dr. Curry on this point. To the extent that the atmosphere is conceived as a static radiative insulating blanket in respect to the surface, one would expect warming of the surface with increased concentration of CO2. But more realistically conceived as a heat engine, driven by intense radiative coupling with the surface, one has to consider the possibility that an increased concentration of CO2 improves the effectiveness of the working fluid of the machine. The heat engine should perform better with a slightly more intense radiative coupling at the surface, so why would it be impossible to drive the same amount of heat to high altitude with a slightly lower surface temperature?
I made a machine learning app that predicts,for 20 by 20 km patches of earth, monthly and annual average temperatures from monthly average surface and top of atmosphere visible power flux, altitude, monthly rainfall, surface albedo and a few other inputs. . The resulting learned function is differentiable and can be used to calculate climate sensitivity to visible light and, after accounting for surface albedo, sensitivity to IR flux. It show sensitivities well below 0.2 C w/m2. Code and lots of graphs are here: https://github.com/ndbucksmith/tf.climate
We have nearly 80 years of good data. We don’t need to guess at this and we can make much better estimates.
Since 1945, Co2 has risen 30% from 310 to 410. Temperatures have risen between 0.3-0.8C depending on the data you use. TCS therefore is between 1.0-2.4C. ECS is not much higher. The reason is simple. We have 75 years of data and if anything the rate of increase since 1945 has been decreasing not increasing even as we add more co2 at higher rates. If there was latent heat or effects in the system we would be seeing that already.
The El Nino of 2015 was an event that triggered high temperatures that have diminished over the last several years as expected. We were told by prominent climatologists that there would be no drop off in temperatures after this El Nino. As usual they were wrong. I was told by a climate modeling leader in my Global Warming class at Stanford years ago that El Ninos would disappear as Co2 effects dominated. Didn’t happen.
We got an El Nino after nearly 15 years of no temperature change. Without that the temperature change over 20 years would have been nearly 0. This is after we are pouring into the atmosphere at the most prodigious rate ever and faster than even their models anticipated.
The highest rate of change in temperatures happened from 1978-1998 which coincided with a positive PDO/AMO cycle and increasing Co2. For the 30 years prior to 1978 temperatures barely moved and may have gone down not up even though this is the period of most active Co2 output in mans history to that time.
About 95% of all Co2 ever produced by man has occurred since 1945. Thus this is the only period we can use as reference.
If you use data prior to 1945 it acts as a control. From 1880-1945 temperatures rose about the same as during the Co2 phase. Thus it is not possible to conclude that Co2 has any effect on temperature. During the period for which we poured in 1/20th the Co2 we got the same temperature change as when we poured in 20 times as much. The two causes can’t be from Co2. Therefore we need to know the specifics of what caused what warming to be able to deduce what %age may have come from Co2 vs other unknown causes.
Climate Scientists cannot explain adequately the decline in temperatures between 1945-1975 while we massively increased Co2. They claim pollution or aerosols. However, studies have shown this is very speculative. Since we don’t know these things and we have the prior 80 year period exhibiting the same temperature change with virtually no co2 increase we can conclude that the sensitivity of the atmosphere to Co2 could very well be only a fraction of the 1.0-2.4 TCS range.
When this alarmism started we saw graphs showing 8C change from a little over 100ppm change just since 1945. Instead we’ve seen 0.3-0.8C or 1/10th the effect they postulated. Their models are actually conservative compared to the theories they had. Yet, today we see the models are hot, very hot compared to reality.
Yes, it is remotely possible that ice fields might suddenly collapse and disintegrate or that giant volcanoes will go off and asteroids will strike the Earth or that any number of things might go wrong.
It would be unscientific to quote a change in temperature greater than we’ve seen in the last 75 years for the next 75 years given the Co2 levels will rise a similar 30% to the last 75 years. This is how the atmosphere reacts to Co2 and its proven by 75 years of data. To say anything else is not scientific.
This is not really in dispute.