climateetc--2020-04-26--A sensible COVID-19 exit strategy for the UK

A study by the COVID-19 Response Team from Imperial College ( Ferguson et al .
2020 [ i ] ) appears to be largely responsible for driving UK government policy actions .
The lockdown imposed in the UK appears , unsurprisingly , to have slowed the growth of COVID-19 infections , and may well soon lead to total active cases declining .
However , it comes at huge economic and social costs , and substantial COVID-19-unrelated health costs .
Worse , the lockdown is merely a holding strategy , which offers no long term solution to the COVID-19 problem .
@ @ @ @ @ @ @ COVID-19 are not reduced relative to any less restrictive policy that likewise avoided the health system being overwhelmed .
Deaths are merely spread over a longer period , assuming that eventually restrictions are lifted and people ’ s lives return to normal .
Vaccinating the population against COVID-19 is unlikely to be achieved for 15-18 months at best .
A repurposed existing drug might be found to work on a shorter timescale , but a sensible strategy can not rely on that hope .
Developing and testing a successful new drug would likely @ @ @ @ @ @ @ no guarantee that a vaccine or drug effective against COVID-19 will be found in the foreseeable future .
Ferguson et al .
illustrates an adaptive cyclical on-off triggering of suppression strategies – involving lockdown approximately two-thirds of the time – extending to the end of 2021 .
But by that time their model implies that under 2 % of the population has been infected and acquired immunity , whereas 80+ % of the population needs to have been infected in order to achieve herd immunity in the absence of any restrictions .
It @ @ @ @ @ @ @ years living under an on-off lockdown regime to achieve that level .
Clearly , the Ferguson et al .
illustrated on-off lockdown strategy is not appropriate .
A more intelligent approach is needed .
Fortunately , there is an obvious solution .
The key is to remove restrictions from those segments of the population that are at low risk of death from COVID-19 infection .
Age is a key factor here .
However , another key factor is whether a person suffers from various chronic health conditions , the most prevalent of which @ @ @ @ @ @ @ , atrial fibrillation , obstructive pulmonary disease , and renal failure ( “ relevant health conditions ” ) .
Over half the population are under 70 years old and do not suffer from any chronic health conditions that are associated with a much elevated infection fatality rate ( IFR ) .
The IFR that I estimate for that segment of the population is only 0.03 % .
So is the estimated IFR for people under 30 years old who have one or more relevant health conditions .
I estimate that there are over @ @ @ @ @ @ @ with no relevant health conditions , or have such conditions but are under 30 .
Allowing them to resume normal life , subject to some precautions , should lead to around 87 % of them being exposed to COVID-19 and , if susceptible to it , infected , over the next few months .
Because the IFR for these groups is very low , the resulting likely number of deaths would be relatively modest – slightly over 10,000 .
That would represent under 2 % of the expected total deaths in the UK @ @ @ @ @ @ @ estimates that Ferguson et al .
are using turn out to be too high , as looks increasingly likely , there could be substantially fewer deaths .
Assuming that these 10,000 deaths were spread over six months , on average slightly over 2,000 ICU beds would be occupied .
The extent of precautions taken could be varied over time to achieve an even ICU bed occupancy level .
If it turned out that a significant proportion of the population was already not susceptible to COVID-19 infection , the number of deaths could be @ @ @ @ @ @ @ about six months during which the non-vulnerable population was exposed to COVID-19 infection , vulnerable groups ( people over 30 year olds with any relevant health condition , and all over 70 year olds ) would need to remain fairly isolated from other people who remained susceptible to COVID-19 .
By the end of that period approximately 54 % of the population would no longer be susceptible to COVID-19 .
That is sufficient to provide herd immunity if the reproduction ratio is below 1.5 .
The fact that daily new cases of COVID-19 @ @ @ @ @ @ @ the end of March 2020 strongly suggests that the relatively limited measures taken there have reduced the reproduction ratio to well below 1.5 , despite that being totally at variance with the much smaller reduction that the Imperial College Response Team ’ s modelling in another study ( Flaxman et al 2020 ) [ ii ] implies .
Therefore , weaker measures than those in force in Sweden at that time should be adequate to prevent any resurgence of COVID-19 infections in the UK if 54 % herd immunity is achieved .
My @ @ @ @ @ @ @ % of the UK population to immediately resume something close to normal life , with the other , more vulnerable , groups being able to do so , subject to some precautions ( which could be on an advisory rather than mandatory basis ) within six months .
In contrast , the current policy in the UK , which aims for all parts of the population to avoid exposure to COVID-19 , will – until and unless an effective vaccine is available – take multiple years to achieve a sufficient level of herd @ @ @ @ @ @ @ effective in preventing a resurgence of infections .
The IFR estimates that I use are based on those given in another paper by the same team at Imperial ( Verity et al.2020 [ iii ] ) , on which the Ferguson et al .
assumptions were based , and on other published findings .
The ICU bed occupation estimates that I use are based on the assumptions in Ferguson et al .
Further details of my results and the data and assumptions involved are available here .
[ i ] Neil M Ferguson @ @ @ @ @ @ @ ( NPIs ) to reduce COVID-19 mortality and healthcare demand , Imperial College COVID-19 Response Team Report 9 , 16 March 2020 , https : //spiral.imperial.ac.uk:8443/handle/10044/1/77482 [ ii ] Flaxman , Seth , et al .
“ Report 13 : Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in 11 European countries.
” ( 2020 ) .
https : //spiral.imperial.ac.uk/bitstream/10044/1/77731/9/2020-03-30-COVID19-Report-13.pdf [ iii ] Verity R , Okell LC , Dorigatti I , et al .
Estimates of the severity of COVID-19 disease .
medRxiv 13 March 2020 ; @ @ @ @

By Nic Lewis
The current approach
A study by the COVID-19 Response Team from Imperial College (Ferguson et al. 2020[i]) appears to be largely responsible for driving UK government policy actions. The lockdown imposed in the UK appears, unsurprisingly, to have slowed the growth of COVID-19 infections, and may well soon lead to total active cases declining. However, it comes at huge economic and social costs, and substantial COVID-19-unrelated health costs.
Worse, the lockdown is merely a holding strategy, which offers no long term solution to the COVID-19 problem. The eventual total number of deaths for COVID-19 are not reduced relative to any less restrictive policy that likewise avoided the health system being overwhelmed. Deaths are merely spread over a longer period, assuming that eventually restrictions are lifted and people’s lives return to normal.
Vaccinating the population against COVID-19 is unlikely to be achieved for 15-18 months at best. A repurposed existing drug might be found to work on a shorter timescale, but a sensible strategy cannot rely on that hope. Developing and testing a successful new drug would likely take longer. Worse, there is no guarantee that a vaccine or drug effective against COVID-19 will be found in the foreseeable future.
Ferguson et al. illustrates an adaptive cyclical on-off triggering of suppression strategies – involving lockdown approximately two-thirds of the time – extending to the end of 2021. But by that time their model implies that under 2% of the population has been infected and acquired immunity, whereas 80+% of the population needs to have been infected in order to achieve herd immunity in the absence of any restrictions. It would take of the order of 70 years living under an on-off lockdown regime to achieve that level.
A sensible approach
Clearly, the Ferguson et al. illustrated on-off lockdown strategy is not appropriate. A more intelligent approach is needed. Fortunately, there is an obvious solution. The key is to remove restrictions from those segments of the population that are at low risk of death from COVID-19 infection. Age is a key factor here. However, another key factor is whether a person suffers from various chronic health conditions, the most prevalent of which are hypertension, diabetes, cardiovascular disease, atrial fibrillation, obstructive pulmonary disease, and renal failure (“relevant health conditions”).
Over half the population are under 70 years old and do not suffer from any chronic health conditions that are associated with a much elevated infection fatality rate (IFR). The IFR that I estimate for that segment of the population is only 0.03%. So is the estimated IFR for people under 30 years old who have one or more relevant health conditions.
I estimate that there are over 41 million people who are under 70 with no relevant health conditions, or have such conditions but are under 30. Allowing them to resume normal life, subject to some precautions, should lead to around 87% of them being exposed to COVID-19 and, if susceptible to it, infected, over the next few months. Because the IFR for these groups is very low, the resulting likely number of deaths would be relatively modest – slightly over 10,000. That would represent under 2% of the expected total deaths in the UK during 2020. And if the IFR estimates that Ferguson et al. are using turn out to be too high, as looks increasingly likely, there could be substantially fewer deaths.
Assuming that these 10,000 deaths were spread over six months, on average slightly over 2,000 ICU beds would be occupied. The extent of precautions taken could be varied over time to achieve an even ICU bed occupancy level. If it turned out that a significant proportion of the population was already not susceptible to COVID-19 infection, the number of deaths could be substantially lower.
During the period of about six months during which the non-vulnerable population was exposed to COVID-19 infection, vulnerable groups (people over 30 year olds with any relevant health condition, and all over 70 year olds) would need to remain fairly isolated from other people who remained susceptible to COVID-19. By the end of that period approximately 54% of the population would no longer be susceptible to COVID-19. That is sufficient to provide herd immunity if the reproduction ratio is below 1.5.
The fact that daily new cases of COVID-19 have not been increasing in Sweden since the end of March 2020 strongly suggests that the relatively limited measures taken there have reduced the reproduction ratio to well below 1.5, despite that being totally at variance with the much smaller reduction that the Imperial College Response Team’s modelling in another study (Flaxman et al 2020)[ii] implies. Therefore, weaker measures than those in force in Sweden at that time should be adequate to prevent any resurgence of COVID-19 infections in the UK if 54% herd immunity is achieved.
My proposed exit strategy would enable over 60% of the UK population to immediately resume something close to normal life, with the other, more vulnerable, groups being able to do so, subject to some precautions (which could be on an advisory rather than mandatory basis) within six months.
In contrast, the current policy in the UK, which aims for all parts of the population to avoid exposure to COVID-19, will – until and unless an effective vaccine is available – take multiple years to achieve a sufficient level of herd immunity for relatively limited measures to be effective in preventing a resurgence of infections.
The IFR estimates that I use are based on those given in another paper by the same team at Imperial (Verity et al.2020 [iii]), on which the Ferguson et al. assumptions were based, and on other published findings. The ICU bed occupation estimates that I use are based on the assumptions in Ferguson et al. Further details of my results and the data and assumptions involved are available here.
Nicholas Lewis 26 April 2020
Originally posted here
References and Notes
[i] Neil M Ferguson et al., Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand, Imperial College COVID-19 Response Team Report 9, 16 March 2020, https://spiral.imperial.ac.uk:8443/handle/10044/1/77482
[ii] Flaxman, Seth, et al. “Report 13: Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in 11 European countries.” (2020). https://spiral.imperial.ac.uk/bitstream/10044/1/77731/9/2020-03-30-COVID19-Report-13.pdf
[iii] Verity R, Okell LC, Dorigatti I, et al. Estimates of the severity of COVID-19 disease. medRxiv 13 March 2020; https://www.medrxiv.org/content/10.1101/2020.03.09.20033357v1.
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climateetc--2020-04-28--Apparent Paradoxes in the relationship of Climate ‘Concerns, Skepticism, Activism, and Priority’, ex

Apparent Paradoxes in the relationship of Climate ‘Concerns, Skepticism, Activism, and Priority’, explained by Religiosity

Explores the contrast between Allied and Core belief in the culture of climate catastrophe , and the relationships of these plus religiosity to Climate Change Activism ( XR and Children ’ s Strikes for Climate ) .
Post 2 of 3 .
The opening post of this series demonstrated a strong correlation across nations between religiosity , and the responses per nation to unconstrained questions from a 2019 YouGov survey on attitudes to climate-change , which questions are aligned to Catastrophic Climate Change Culture ( CCCC ) .
Chart 1 below was shown towards the end of the prior post @ @ @ @ @ @ @ of ‘ a great deal ’ responses to two climate survey questions : “ How much of an impact , if any , do you believe climate change will have on your life ?
” ( blue series ) , and “ how much power , if any , do you think each of the following have to combat climate change ?
” sub-option , “ International bodies ( e.g .
the United Nations ) ” ( pink series ) .
I term the effect causing these trends ‘ Allied Belief ’ ( @ @ @ @ @ @ @ surface alliance between CCCC and religion , makes religious adherents feel comfortable with climate catastrophe narratives , as long as there are no reality constraints , thereby disabling their Innate Skepticism of CCCC .
As religiosity rises within nations ( going left to right ) , ISk about CCCC narratives falls , and so belief in said narratives rises .
The pink series is muted , as it ’ s only needed to show lower gradient for less emotive / existential / personal questions .
Note : ISk is an instinctive mechanism that @ @ @ @ @ @ @ [ Chart is simplified Chart 2 in SI datafile ] .
So , if we think of what we might call ‘ standard belief ’ in a culture , especially strong expressions of this such as we ’ re very familiar with from ardent religious adherents , or indeed regarding the case in point , those similarly committed to catastrophic climate culture , how would that look on the above chart and how does it differ from ABel ?
We can start with the fact that adherence of this kind doesn ’ t @ @ @ @ @ @ @ s a direct commitment , essentially to core cultural narratives ( from which we can label it Core Belief , CBel ) and one that survives clashes with reality issues .
So , now it ’ s time to look at reality-constrained survey questions .
The orange series in Chart 2 plots results from a reality-constrained question , in fact the enormous 2015 UN ‘ My World ’ poll having ~10 million participants from many nations .
In this case , constraint arises from having to rank 6 global threats out of 17 @ @ @ @ @ @ @ being ‘ action on climate change ’ .
Note : the orange scale , the percentage vote-share for action on climate change from this poll , is smaller than the blue .
Nations correspond vertically ; in this chart ( and above too ) Taiwan and Hong-Kong are dropped as the UN poll doesn ’ t cover them , leaving 22 nations .
[ Chart is Chart 2yxA in SI datafile ] .
So , there ’ s apparent paradoxes here !
Those nations expressing the highest concerns about climate-change ( and inclusive @ @ @ @ @ @ @ fix it ) , also express the lowest desire for ‘ climate-change action ’ .
And oppositely at the LHS .
However , this fits fine with the underlying cultural mechanics .
Also , it looks like there ’ s structure in the wide variability about the orange trend .
Before saying more about these things though , we must continue to pursue CBel… The orange series represents belief in a weakly-constrained circumstance ( so I term that Constrained Belief , ConBel ) .
There ’ s much variety in how surveys questions @ @ @ @ @ @ @ for commonly used issue lists : How many issues ?
How many to select out of them ?
Are they all genuinely felt concerns ?
How closely felt ( e.g .
national versus international , for the former , country relevant ? )
.
The SI ( especially Footnote 4 ) provides detail .
This will produce a range of constraint strengths .
For a choice of X out of Y issues , constraint is stronger as X reduces relative to Y ( so say , 1 out of 12 much stronger than @ @ @ @ @ @ @ to the winning issue ( s ) has to be higher .
National issues are stronger than global ones ( closer to reality for most participants ) .
To find CBel , all we have to do is increase the constraint strength to ‘ ultimate ’ as it were .
If people still believe in action on climate change above all other reality issues however strong , this indicates a direct belief in the narratives of catastrophe and salvation , which in their own terms and contrary to mainstream science , do outbid @ @ @ @ @ @ @ can ’ t measure this , because there ’ s no survey with a consistent method of full constraint across many nations ( that I ’ m aware of ) .
But we can estimate it , and we can also box the estimate in via nearby measurements .
Hence I add a series ( red ) to Chart 2 , which represents those still choosing climate-change in a strongly-constrained ( but not fully ) survey over 16 of the 22 nations ( most I can get ) .
And I add some @ @ @ @ @ @ @ few European nations ( red crosses ) , that are fully constrained .
This gives Chart 3 below , on which I also place an estimate of CBel ( bold-orange ) .
Note : for clarity only the trend of the red series is shown .
The orange series above , on expanded RH Y axis , is also reduced to just its trend .
[ For full details , data , r/r2/p , survey-sources , see Chart 3yxA of datafile and Section 3 of expanded post ] .
A very straightforward estimate @ @ @ @ @ @ @ UN Poll vote-share for action on climate-change ( orange series in Chart 2 ) by 6 ; the reasonable assumption of evenly distributed votes on the selected issues means this is how many would have chosen that option as their top priority .
While being asked to make that top single-choice directly is a different context , which may skew the estimate somewhat , it is below the red trend and with ~proportional gradient as we expect , plus the red actual measurements ( crosses ) at the LHS , do straddle the @ @ @ @ @ @ @ is a little high there , however I take the bold-orange trend as my CBel going forward .
On a single chart we now have ABel ( with muted-pink showing how it tracks when weaker ) , and CBel ( equivalent to full strength ConBel , with muted-red plus orange showing how it tracks when weaker ) .
It ’ s worth noting that as core believers will be affirmative to all climate-change issues / concerns , the ABel trends are actually inclusive of CBel too , albeit the latter is very much @ @ @ @ @ @ @ So , back to that apparent paradox noted above .
A reality-constraint has two effects on climate-change attitudes .
Firstly , the issue is forced down to Earth so to speak , robbing a great deal of the emotive power in climate catastrophe narratives .
At the same time , cost and consequence is introduced wrt all the other important issues that are needful in our societies , in turn challenging existing personal value-sets that are set within the context of those societies .
Although the UN poll constrains pretty weakly , the @ @ @ @ @ @ @ CCCC narrative is enough to dissolve the subconscious reassurance in religious folks that this cause is truly allied to their values , to their faith .
Presumably , they don ’ t subconsciously feel the oft-proclaimed alliance any longer .
Hence their ISk , which had been disabled by this reassurance , springs into action .
As religious adherents have strongly focused cultural values to defend , which are now challenged by the societal consequences / costs , their ISk comes in big .
Subconsciously biased communicators for or against climate-change issues / @ @ @ @ @ @ @ at various places on Chart 3 trendlines .
This increases confusion about what publics actually think , and cultures can take advantage of confusion .
I ’ d be surprised though if communicators have any inkling about the global role of religiosity ; no doubt they ’ ll see what they want to regarding motivations both ‘ good ’ and ‘ bad ’ .
However , a huge problem for CCCC adherents within irreligious nations , is that innate skepticism of any narratives that veer too much towards the existential / emotive , @ @ @ @ @ @ @ huge .
Yet cultures don ’ t work rationally and propagate / amplify such narratives at every opportunity , turning off a big majority of people even as they also gain adherents .
Similarly , any real-world strong constraint such as the sacrifice of petrol cars or gas heating , is likely only to achieve CBel levels of support at best .
Though all trendlines change with time too ( CCCC is growing ) even the more modest sacrifices won ’ t likely get support beyond the strongly-constrained ( red ) trendline– if @ @ @ @ @ @ @ realized , which may not necessarily be the case .
[ Note : the huge new reality-constraint of COVID-19 will highly likely squeeze the thick orange line down still further wrt other priorities ; minimal fervent believers only , albeit this effect could evaporate after a year or two .
Cultures are typically very robust to such damage , and may even find ways to turn adverse conditions into advantage longer-term ] .
This leads to enormous frustration for core believers within irreligious countries , but far less for those in religious countries @ @ @ @ @ @ @ ( albeit being from ABel , it ’ s ephemeral ) .
So regarding Climate Activism , we expect this to occur most in nations at the LHS of Chart 3 , where core believers are defending their existential culture from the great majority of non-believers ; and for cultures as for armies sometimes , attack ( aggressive proselytization , civil disobedience ) is a good form of defense .
In very religious countries , there ’ s far less apparent need to rail against society , and less core believers to start @ @ @ @ @ @ @ So , we expect proportionally high activism at left , to low at right .
As explained in the SI , for any route attempting to demonstrate that climate activism conforms to this pattern , available data seems far-from-ideal .
I went with the route of sampling Extinction Rebellion and Children ’ s Strike Weekly presence across nations , from their respective websites .
I split the XR data into tiers , with Tier 3 the highest presence ( > 0.5 groups per million population ) , Tier 2 next lowest ( > @ @ @ @ @ @ @ , and Tier 0 with zero presence .
[ For how / why / details , see SI expanded post Section 5 and Footnote 6 ] .
These Tiers mapped onto the main ABel / CBel trends in Chart 4 below , do show the highest XR presence Tier ( dark-green ) at the leftmost side , and the second-most presence Tier ( mid-green ) covering the next few nations to the left .
Excepting Qatar , which happens to have 1 XR group and a very low population , a lucky T2-score @ @ @ @ @ @ @ is far lower , figures in pale green show fractions of the presence in Italy , the least represented nation in Tier 2 .
Given the rough data , this is at least consistent with motivational expectations from above .
Implications for youth , and Children ’ s Strike conformance to expectation The top-ten ranking of Children ’ s Strike Weekly ( CSW ) events ( as a ratio of national population ) per nation , is mapped onto Chart 4 .
See the numbers trailing the nation name-labels ( 1=highest ) .
@ @ @ @ @ @ @ the leftmost nations , matching the section 4 expectation , albeit nations are ordered differently within the top-ten .
The data is likely too rough to expect exact correspondence ( of CSW to XR rank per nation , or of either to the exact trend prediction ) .
However despite this , it ’ s noticeable that Spain and Italy seem rather far rightwards / religious to achieve CSW scores of 4 and 5 respectively , which brings us to an issue specific to youth .
In Western nations where religion is receding @ @ @ @ @ @ @ rankers ) , young adults are considerably less religious than older adults .
While there are very few surveys involving children , this rule presumably extends downwards in age , given this is where young adults recently came from .
So at the national level , children will behave as though they come from a nation rather to the left on Chart 4 of their actual home nation .
Which is to say , as a group having more CBel and more climate activism , yet simultaneously less ABel so more skepticism .
@ @ @ @ @ @ @ side of this is , as noted within this previous post , children are primed to pick up cultural templates ( as provided by CCCC ) , but may not have developed a balancing ISk ( yet , or ever ) .
So , children ’ s CBel may be even more amplified .
The religiosity gap separating children and adults can also be different per nation , which will explain at least some CSW ranking changes relative to XR , also to any data reflecting mainly adult attitudes .
Based upon projection @ @ @ @ @ @ @ Spain does indeed have an unusually big gap .
This would explain Spain ’ s high CSW ranking .
However , the same doesn ’ t hold for Italy , unless a similar strength effect for children hasn ’ t yet surfaced as they mature to adults .
[ See SI Footnote 7 and datafile for CSW details / data / source , plus Footnote 8 for religiosity gap ] .
As noted in Section 2 , variability about the UN poll ConBel / CBel trend ( all orange series in Chart 2/3 @ @ @ @ @ @ @ the ( opposite direction ) ABel trend ( blue ) .
To investigate this further ( is it noise ?
something systemic ?
something to do with individual faiths ? )
, I created Chart 5 below .
This adds many more nations from the UN poll ( but not covered by the main Climate Survey ) .
The Y scale happens to match estimated CBel .
The chart is split into 4 main religio-regional blocks , a ) to d ) , within which further subdivisions ( religio-regional groups ) are color-coded @ @ @ @ @ @ @ c ) , so I ’ ve assisted the eye with line connections for a couple of groups .
Note : greyed-out nations are too unique or too far from any others to associate in a religio-regional group .
[ See Chart 5yx in SI datafile for full data ] .
This representation helps show the regional rather than Faith-related nature of our evolution away from religion , albeit some regions / Faiths essentially correspond .
Hence , we will see regionality in attitudes to climate-change that result from the interaction of religion @ @ @ @ @ @ @ main trend is so large it can ’ t be contained , so to speak , by some of the less sizeable Faiths or regions .
Meaning that looking exclusively inside either , the main trend may not just appear weak , but non-existent or reversed .
However , it ’ s clear from this depiction [ and a comparative Faiths only color-coding , see SI datafile Chart 4yx ] that the variability isn ’ t caused by Faiths or regionality either .
So , where does it from ?
It turns out @ @ @ @ @ @ @ coming from GDP-per-Capita ranking ( GDPpCR ) .
9 of 13 ( not greyed out ) nations forming the bottom-most band of the trend are below low ( high number ) GDPpCR thresholds per group [ and for a ) and b ) , per block too ] .
There ’ s also some highest ( low number ) GDPpCR nations per group on the uppermost edge , plus an upper threshold in block a ) .
[ SI Footnote 10 has detail on the GDPpC aspect ] .
Likely , the remaining @ @ @ @ @ @ @ a given religiosity , the publics of less-wealthy nations ( lower GDPpCR ) , are even less disposed to transfer any serious cultural allegiance from their religious faith to CCCC .
This is opposite to what one would expect if these publics were clamoring for more climate-change $ from richer nations , albeit some governments may be .
The more pressured financial circumstances will sharpen reality-constraints still further , leading to still more ISk and less conversions into Core Belief .
Such cultural mechanisms easily outbid a dream of filtered down $ .
@ @ @ @ @ @ @ towards the top-left in Charts 4 & 5 , is the national Electric Vehicle sales ranking .
The next post covers cultural expectations for these figures .
Affirmative Public attitudes to Climate-Change are cultural not rational The positions of nations on the various trendlines shown here represent the most affirmative attitudes to climate-change issues , and are all due to cultural not rational responses .
Even the secondary GDPpCR angle that causes variation about the ConBel / CBel trend , is only the exacerbation of a cultural factor .
These positions aren ’ @ @ @ @ @ @ @ or the potential exposure of particular nations to any actual climate impacts .
Knowing only national religiosity , allows a reasonable prediction of these CC affirmative attitudes , albeit for reality-constrained questions GDP-per-capita also sharpens the prediction .
Prediction isn ’ t a goal in itself , but if verified and accurate , valuably confirms what ’ s actually happening .
For the religious ( so mainly as revealed in religious nations ) , CCCC appears to be perceived as potentially competitive ; their deep values aren ’ t shifting to it despite @ @ @ @ @ @ @ to disable their Innate Skepticism .
Within irreligious nations , CCCC appears to be partially filling the recent cultural vacuum .
All this leads to apparent paradoxes .
In highly religious nations , the most climate concern lives simultaneously with the least priority , each dependent only upon the survey question types ; maximum activism occurs within those nations having the most skepticism and so the least concern about climate-change in unconstrained questions .
Yet as explained above , these paradoxical attitudes do follow the logic of cultural mechanisms .
Table 1 below @ @ @ @ @ @ @ ‘ Strongly-Framed ’ questions , i.e .
reality-constrained questions where the constraint is better than ‘ very weak ’ , or unconstrained questions where the emotive / existential / personal alignment to CCCC is better than ‘ very weak ’ .
The final post shows that even affirmatives to Weakly-Framed climate survey questions , such as those encountered in the first post of this series , produce cultural not rational responses , albeit non-linear wrt religiosity .
It also looks at elite attitudes and consequent policy penetration , plus further at the influence @ @ @ @ @ @ @ findings across the whole series .
There are 3 posts in this series , all of which have the same style of Supplementary Information , which consist : 1 ) an expanded post , 2 ) a footnotes file , and 3 ) an Excel datafile .
The text below is a streamlined post version , geared to get the concepts across more readily and uncluttered regarding side-issues , detail on methodology , intricate depth , path my exploration took etc .
For folks who want more , the expanded post is ~4700 @ @ @ @ @ @ @ file , also having various external references , relates to the expanded post ( though a couple are pointed at below ) .
Likewise , all the chart IDs within the Excel datafile are numbered for the expanded post .
However , all sources / data for the charts below can easily be found ( I provided SI IDs in the text ) .
The datafile includes various extra charts too .

by Andy West
Explores the contrast between Allied and Core belief in the culture of climate catastrophe, and the relationships of these plus religiosity to Climate Change Activism (XR and Children’s Strikes for Climate). Post 2 of 3.
Introduction
The opening post of this series demonstrated a strong correlation across nations between religiosity, and the responses per nation to unconstrained questions from a 2019 YouGov survey on attitudes to climate-change, which questions are aligned to Catastrophic Climate Change Culture (CCCC). Chart 1 below was shown towards the end of the prior post. Y axis values show the percentage of ‘a great deal’ responses to two climate survey questions: “How much of an impact, if any, do you believe climate change will have on your life?” (blue series), and “how much power, if any, do you think each of the following have to combat climate change?” sub-option, “International bodies (e.g. the United Nations)” (pink series).
I term the effect causing these trends ‘Allied Belief’ (ABel). They occur because the surface alliance between CCCC and religion, makes religious adherents feel comfortable with climate catastrophe narratives, as long as there are no reality constraints, thereby disabling their Innate Skepticism of CCCC. As religiosity rises within nations (going left to right), ISk about CCCC narratives falls, and so belief in said narratives rises. The pink series is muted, as it’s only needed to show lower gradient for less emotive / existential / personal questions. Note: ISk is an instinctive mechanism that is very different to rational skepticism. [Chart is simplified Chart 2 in SI datafile].
Constrained Belief and Core Belief
So, if we think of what we might call ‘standard belief’ in a culture, especially strong expressions of this such as we’re very familiar with from ardent religious adherents, or indeed regarding the case in point, those similarly committed to catastrophic climate culture, how would that look on the above chart and how does it differ from ABel? We can start with the fact that adherence of this kind doesn’t primarily occur via alliance; it’s a direct commitment, essentially to core cultural narratives (from which we can label it Core Belief, CBel) and one that survives clashes with reality issues. So, now it’s time to look at reality-constrained survey questions.
The orange series in Chart 2 plots results from a reality-constrained question, in fact the enormous 2015 UN ‘My World’ poll having ~10 million participants from many nations. In this case, constraint arises from having to rank 6 global threats out of 17 as the most important, with one being ‘action on climate change’. Note: the orange scale, the percentage vote-share for action on climate change from this poll, is smaller than the blue. Nations correspond vertically; in this chart (and above too) Taiwan and Hong-Kong are dropped as the UN poll doesn’t cover them, leaving 22 nations. [Chart is Chart 2yxA in SI datafile].
So, there’s apparent paradoxes here! Those nations expressing the highest concerns about climate-change (and inclusive of more faith in the UN to fix it), also express the lowest desire for ‘climate-change action’. And oppositely at the LHS. However, this fits fine with the underlying cultural mechanics. Also, it looks like there’s structure in the wide variability about the orange trend. Before saying more about these things though, we must continue to pursue CBel…
The orange series represents belief in a weakly-constrained circumstance (so I term that Constrained Belief, ConBel). There’s much variety in how surveys questions can constrain. For instance, even for commonly used issue lists: How many issues? How many to select out of them? Are they all genuinely felt concerns? How closely felt (e.g. national versus international, for the former, country relevant?). The SI (especially Footnote 4) provides detail. This will produce a range of constraint strengths. For a choice of X out of Y issues, constraint is stronger as X reduces relative to Y (so say, 1 out of 12 much stronger than 3 out of 12), commitment to the winning issue(s) has to be higher. National issues are stronger than global ones (closer to reality for most participants).
To find CBel, all we have to do is increase the constraint strength to ‘ultimate’ as it were. If people still believe in action on climate change above all other reality issues however strong, this indicates a direct belief in the narratives of catastrophe and salvation, which in their own terms and contrary to mainstream science, do outbid everything else. The snag is we can’t measure this, because there’s no survey with a consistent method of full constraint across many nations (that I’m aware of). But we can estimate it, and we can also box the estimate in via nearby measurements.
Hence I add a series (red) to Chart 2, which represents those still choosing climate-change in a strongly-constrained (but not fully) survey over 16 of the 22 nations (most I can get). And I add some spot-points for much smaller surveys on a few European nations (red crosses), that are fully constrained. This gives Chart 3 below, on which I also place an estimate of CBel (bold-orange). Note: for clarity only the trend of the red series is shown. The orange series above, on expanded RH Y axis, is also reduced to just its trend. [For full details, data, r/r2/p, survey-sources, see Chart 3yxA of datafile and Section 3 of expanded post].
A very straightforward estimate of CBel is obtained by dividing the UN Poll vote-share for action on climate-change (orange series in Chart 2) by 6; the reasonable assumption of evenly distributed votes on the selected issues means this is how many would have chosen that option as their top priority. While being asked to make that top single-choice directly is a different context, which may skew the estimate somewhat, it is below the red trend and with ~proportional gradient as we expect, plus the red actual measurements (crosses) at the LHS, do straddle the estimate. The latter hint the estimate is a little high there, however I take the bold-orange trend as my CBel going forward.
On a single chart we now have ABel (with muted-pink showing how it tracks when weaker), and CBel (equivalent to full strength ConBel, with muted-red plus orange showing how it tracks when weaker). It’s worth noting that as core believers will be affirmative to all climate-change issues / concerns, the ABel trends are actually inclusive of CBel too, albeit the latter is very much smaller across most of the chart.
ConBel / CBel underlying mechanism
So, back to that apparent paradox noted above. A reality-constraint has two effects on climate-change attitudes. Firstly, the issue is forced down to Earth so to speak, robbing a great deal of the emotive power in climate catastrophe narratives. At the same time, cost and consequence is introduced wrt all the other important issues that are needful in our societies, in turn challenging existing personal value-sets that are set within the context of those societies.
Although the UN poll constrains pretty weakly, the loss of emotive power / alignment to CCCC narrative is enough to dissolve the subconscious reassurance in religious folks that this cause is truly allied to their values, to their faith. Presumably, they don’t subconsciously feel the oft-proclaimed alliance any longer. Hence their ISk, which had been disabled by this reassurance, springs into action. As religious adherents have strongly focused cultural values to defend, which are now challenged by the societal consequences / costs, their ISk comes in big.
Real-world implications: Policy and Climate Activism
Subconsciously biased communicators for or against climate-change issues / support, can find comfort and claims at various places on Chart 3 trendlines. This increases confusion about what publics actually think, and cultures can take advantage of confusion. I’d be surprised though if communicators have any inkling about the global role of religiosity; no doubt they’ll see what they want to regarding motivations both ‘good’ and ‘bad’. However, a huge problem for CCCC adherents within irreligious nations, is that innate skepticism of any narratives that veer too much towards the existential / emotive, especially with any personal angle, is huge. Yet cultures don’t work rationally and propagate / amplify such narratives at every opportunity, turning off a big majority of people even as they also gain adherents. Similarly, any real-world strong constraint such as the sacrifice of petrol cars or gas heating, is likely only to achieve CBel levels of support at best. Though all trendlines change with time too (CCCC is growing) even the more modest sacrifices won’t likely get support beyond the strongly-constrained (red) trendline– if the real-world clashes are properly communicated / realized, which may not necessarily be the case.
[Note: the huge new reality-constraint of COVID-19 will highly likely squeeze the thick orange line down still further wrt other priorities; minimal fervent believers only, albeit this effect could evaporate after a year or two. Cultures are typically very robust to such damage, and may even find ways to turn adverse conditions into advantage longer-term].
This leads to enormous frustration for core believers within irreligious countries, but far less for those in religious countries where support will often seem very high (albeit being from ABel, it’s ephemeral). So regarding Climate Activism, we expect this to occur most in nations at the LHS of Chart 3, where core believers are defending their existential culture from the great majority of non-believers; and for cultures as for armies sometimes, attack (aggressive proselytization, civil disobedience) is a good form of defense. In very religious countries, there’s far less apparent need to rail against society, and less core believers to start with in order to maintain activism. So, we expect proportionally high activism at left, to low at right.
As explained in the SI, for any route attempting to demonstrate that climate activism conforms to this pattern, available data seems far-from-ideal. I went with the route of sampling Extinction Rebellion and Children’s Strike Weekly presence across nations, from their respective websites.
Extinction Rebellion conformance to expectation
I split the XR data into tiers, with Tier 3 the highest presence (>0.5 groups per million population), Tier 2 next lowest (>0.1 groups/M), Tier 1 non-zero, and Tier 0 with zero presence. [For how / why / details, see SI expanded post Section 5 and Footnote 6]. These Tiers mapped onto the main ABel / CBel trends in Chart 4 below, do show the highest XR presence Tier (dark-green) at the leftmost side, and the second-most presence Tier (mid-green) covering the next few nations to the left. Excepting Qatar, which happens to have 1 XR group and a very low population, a lucky T2-score. The XR presence for Tier 1 is far lower, figures in pale green show fractions of the presence in Italy, the least represented nation in Tier 2. Given the rough data, this is at least consistent with motivational expectations from above.
Implications for youth, and Children’s Strike conformance to expectation
The top-ten ranking of Children’s Strike Weekly (CSW) events (as a ratio of national population) per nation, is mapped onto Chart 4. See the numbers trailing the nation name-labels (1=highest). Once again, this ranking picks out the leftmost nations, matching the section 4 expectation, albeit nations are ordered differently within the top-ten. The data is likely too rough to expect exact correspondence (of CSW to XR rank per nation, or of either to the exact trend prediction). However despite this, it’s noticeable that Spain and Italy seem rather far rightwards / religious to achieve CSW scores of 4 and 5 respectively, which brings us to an issue specific to youth.
In Western nations where religion is receding (occurs in all the top-ten CSW rankers), young adults are considerably less religious than older adults. While there are very few surveys involving children, this rule presumably extends downwards in age, given this is where young adults recently came from. So at the national level, children will behave as though they come from a nation rather to the left on Chart 4 of their actual home nation. Which is to say, as a group having more CBel and more climate activism, yet simultaneously less ABel so more skepticism. However, an issue with the ABel side of this is, as noted within this previous post, children are primed to pick up cultural templates (as provided by CCCC), but may not have developed a balancing ISk (yet, or ever). So, children’s CBel may be even more amplified.
The religiosity gap separating children and adults can also be different per nation, which will explain at least some CSW ranking changes relative to XR, also to any data reflecting mainly adult attitudes. Based upon projection from young / older adult data, Spain does indeed have an unusually big gap. This would explain Spain’s high CSW ranking. However, the same doesn’t hold for Italy, unless a similar strength effect for children hasn’t yet surfaced as they mature to adults. [See SI Footnote 7 and datafile for CSW details / data / source, plus Footnote 8 for religiosity gap].
The variability within ConBel / estimated CBel: GDP-per-Capita
As noted in Section 2, variability about the UN poll ConBel / CBel trend (all orange series in Chart 2/3), is significantly larger than for the (opposite direction) ABel trend (blue). To investigate this further (is it noise? something systemic? something to do with individual faiths?), I created Chart 5 below. This adds many more nations from the UN poll (but not covered by the main Climate Survey). The Y scale happens to match estimated CBel.
The chart is split into 4 main religio-regional blocks, a) to d), within which further subdivisions (religio-regional groups) are color-coded. Several of the latter overlap inside c), so I’ve assisted the eye with line connections for a couple of groups. Note: greyed-out nations are too unique or too far from any others to associate in a religio-regional group. [See Chart 5yx in SI datafile for full data].
This representation helps show the regional rather than Faith-related nature of our evolution away from religion, albeit some regions / Faiths essentially correspond. Hence, we will see regionality in attitudes to climate-change that result from the interaction of religion and CCCC. The variability about the main trend is so large it can’t be contained, so to speak, by some of the less sizeable Faiths or regions. Meaning that looking exclusively inside either, the main trend may not just appear weak, but non-existent or reversed. However, it’s clear from this depiction [and a comparative Faiths only color-coding, see SI datafile Chart 4yx] that the variability isn’t caused by Faiths or regionality either. So, where does it from?
It turns out that a lot of the variability is coming from GDP-per-Capita ranking (GDPpCR). 9 of 13 (not greyed out) nations forming the bottom-most band of the trend are below low (high number) GDPpCR thresholds per group [and for a) and b), per block too]. There’s also some highest (low number) GDPpCR nations per group on the uppermost edge, plus an upper threshold in block a). [SI Footnote 10 has detail on the GDPpC aspect]. Likely, the remaining variability is noise.
So, for a given religiosity, the publics of less-wealthy nations (lower GDPpCR), are even less disposed to transfer any serious cultural allegiance from their religious faith to CCCC. This is opposite to what one would expect if these publics were clamoring for more climate-change $ from richer nations, albeit some governments may be. The more pressured financial circumstances will sharpen reality-constraints still further, leading to still more ISk and less conversions into Core Belief. Such cultural mechanisms easily outbid a dream of filtered down $.
The number trailing the name-label of nations towards the top-left in Charts 4 & 5, is the national Electric Vehicle sales ranking. The next post covers cultural expectations for these figures.
Affirmative Public attitudes to Climate-Change are cultural not rational
The positions of nations on the various trendlines shown here represent the most affirmative attitudes to climate-change issues, and are all due to cultural not rational responses. Even the secondary GDPpCR angle that causes variation about the ConBel / CBel trend, is only the exacerbation of a cultural factor. These positions aren’t due to climate science or policies or the potential exposure of particular nations to any actual climate impacts. Knowing only national religiosity, allows a reasonable prediction of these CC affirmative attitudes, albeit for reality-constrained questions GDP-per-capita also sharpens the prediction. Prediction isn’t a goal in itself, but if verified and accurate, valuably confirms what’s actually happening.
For the religious (so mainly as revealed in religious nations), CCCC appears to be perceived as potentially competitive; their deep values aren’t shifting to it despite a cozier surface alliance clearly strong enough to disable their Innate Skepticism. Within irreligious nations, CCCC appears to be partially filling the recent cultural vacuum. All this leads to apparent paradoxes. In highly religious nations, the most climate concern lives simultaneously with the least priority, each dependent only upon the survey question types; maximum activism occurs within those nations having the most skepticism and so the least concern about climate-change in unconstrained questions. Yet as explained above, these paradoxical attitudes do follow the logic of cultural mechanisms.
Table 1 below summarizes the overall pattern of responses to ‘Strongly-Framed’ questions, i.e. reality-constrained questions where the constraint is better than ‘very weak’, or unconstrained questions where the emotive / existential / personal alignment to CCCC is better than ‘very weak’.
The final post shows that even affirmatives to Weakly-Framed climate survey questions, such as those encountered in the first post of this series, produce cultural not rational responses, albeit non-linear wrt religiosity. It also looks at elite attitudes and consequent policy penetration, plus further at the influence of youth, before summarizing the provisional findings across the whole series.
Admin notes
There are 3 posts in this series, all of which have the same style of Supplementary Information, which consist: 1) an expanded post, 2) a footnotes file, and 3) an Excel datafile. The text below is a streamlined post version, geared to get the concepts across more readily and uncluttered regarding side-issues, detail on methodology, intricate depth, path my exploration took etc. For folks who want more, the expanded post is ~4700 words. Be aware that the footnotes file, also having various external references, relates to the expanded post (though a couple are pointed at below). Likewise, all the chart IDs within the Excel datafile are numbered for the expanded post. However, all sources / data for the charts below can easily be found (I provided SI IDs in the text). The datafile includes various extra charts too.
Footnotes [Footnotes ]
Data File [Datafile ]
Extended post [ Extended Post]
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climateetc--2020-11-12--Disconnect in the relationship between GMST and ECS

Abstract .
An analysis is presented of he disconnection between the CMIP5 and CMIP6 Historical and Future periods when considering the relationship of the individual model GMST changes and the climate sensitivity .
I have included a simple model that can account for the period disconnection using the negative forcing of aerosol/cloud effects in the Historical period that is carried forward into the Future period .
I attribute some of the uncertainty in simulations of this simple model to endogenous model decision ( selection ) uncertainty that leads to variations in the changes of the negative forcing in the Historical @ @ @ @ @ @ @ .
There have been a number of references in the climate science literature ( references 1 through10 ) concerning the disconnection between the change in the global mean surface temperature ( GMST ) in the Historical period ( used in this analysis the time period 1850 or 1861 to 2014 ) and the climate sensitivity of models as measured by Equilibrium Climate Sensitivity ( ECS ) and Transient Climate Response ( TCR ) for individual CMIP5 and CMIP6 climate models .
That disconnection has been made more obvious by the CMIP6 ensemble of @ @ @ @ @ @ @ in the CMIP5 ensemble and yet having the near same GMST trends in the Historical period ( references 11 and 12 ) .
While there have been proposed explanations for this disconnection in the literature , mainly pointing to negative aerosol and cloud related forcing in the Historical period as probable causes , there has been neither an analysis of how this effect would carry over into the Future period ( used in this analysis as the 2015-2100 time period ) for individual models nor a direct comparison of the models ’ GMST @ @ @ @ @ @ @ A theme in some of these papers suggests that direct model tuning of the Historical period warming does not account for the forcing compensation for differing model climate sensitivities but rather that it can be produced by selection from a range of parameter processes that can yield overall credible results and in turn a range of credible GMST trends .
If the disconnection stems from a strictly structural difference between individual models , it would not change the concern for the models capability of reproducing the Historical GMST change translating to a capability @ @ @ @ @ @ @ In this analysis I have used the energy budget equation , ΔT= ( ΔF-ΔN ) /λ , where ΔT is the GMST change , ΔF is the forcing change , ΔN is the change in TOA radiative imbalance and λ is the climate feedback parameter for an individual model .
This relationship is further simplified by assuming that the quantity ΔF-ΔN in a correctly modeled form should be near constant for all models in a given scenario and time period and can be replaced by F2x which is the individual model radiative forcing @ @ @ @ @ @ @ .
F2x/ λ is equivalent to ECS but for the purposes of this analysis the term F2x/λ will be used since it better denotes the simplifying relationship it has to the energy budget .
This analysis uses OLS regression of ΔT versus F2x/λ for individual models for a given scenario and time period and ΔT versus ΔT for individual models for scenario versus scenario .
The analysis covers the CMIP5 and CMIP6 Historical and representative Future period scenarios .
Also included in the analysis , as a possible contributing factor in the Historical @ @ @ @ @ @ @ results of OLS regression of ΔN versus ΔN for all scenario combinations .
The critical results derived from all these regressions were the slope t values and r squared values .
It was obvious from the start of this analysis that Future scenario ensembles , where the forcing from greenhouse gases ( GHG ) is dominating over negative aerosol forcing and noise , should have more significant slope t and r squared values than those derived from the Historical period .
In this analysis the expectations of disconnections of the Historical ensemble results @ @ @ @ @ @ @ how this could occur facilitated the construction of simple simulation scenario models ( hereafter in this article referred to as Reproduction Models ) that closely reproduce and provide a potential explanation of the differing model scenario results .
I posted a previous thread here at Climate etc .
( reference 13 ) on the disconnection between the Historical Future scenarios for CMIP5 models .
That analysis did not provide a Reproduction like model showing how a negative forcing can compensate the climate sensitivities in the Historical period and be carried over into the @ @ @ @ @ @ @ there .
I attempt to provide that missing step in this analysis .
All time series changes in climate variables in this analysis were determined using the empirical CEEMDAN method of extracting a secular trend from periodical oscillation and noise components ( references 14 and 15 ) .
This method avoids having to limit the period selections to those where the non-trend components are not thought to bias the variable change .
It also allows the change to be determined for the entire time period selected .
The individual model GMST and TOA @ @ @ @ @ @ @ ( PIC ) for this analysis were taken for the CMIP5 scenarios from KNMI Climate Explorer website ( reference 16 ) and for CMIP6 from the ESGF Node at DKRZ ( reference 17 ) .
The forcing data for CMIP5 were taken from Meinshausen ( reference 18 ) and that for CMIP6 were from Fiedler ( reference 19 ) .
The λ and F2x data were taken from Gregory and Forster ( references 20 and 21 ) for CMIP5 and from Femke ( reference 22 ) for CMIP6 .
The PIC series were @ @ @ @ @ @ @ none were found no adjustments were made to corresponding GMST and TOA series .
There appeared no good reason to subtract non significant components ( noise ) from the series of interest .
A with/without adjustment comparison of the final series changes over the period of interest showed no or very small differences relative to the series changes .
OLS regression was chosen for this analysis after comparing results for the slope and slope t values with Deming regressions .
The small differences in the slope values for these two types of regression @ @ @ @ @ @ @ variable was not sufficient to require use of a version of Total Least Square regression .
The r squared values for the regressions are reported with and without outliers along with the number of outliers found .
These outliers were determined using Cook ’ s Distance criteria of greater than 4 times the mean of the Cook ’ s Distance for all the model results ( reference 23 ) .
No effort was made to analyze the outliers since the motive for detecting them was not to subjectively exclude possible outliers but rather @ @ @ @ @ @ @ regression line and allow comparison where all data points are used .
The residuals from the OLS regressions were tested for auto correlation and fitting to a normal distribution .
Based on those tests , no adjustments to the regression results were required .
Table 1 shows the OLS regression results for the CMIP5 scenarios of Historical , RCP 4.5 , RCP 6.0 and RCP 8.5 and for CMIP6 scenarios of Historical , SSP3 7.0 and the CMIP6 renditions of the CMIP5 RCP 4.5 and 8.5 scenarios .
The ΔT versus ΔT regression @ @ @ @ @ @ @ that for ΔT versus F2x/λ even though differences between the Historical and Future scenarios were expected to produce similar results with both regressions .
With ΔT there were more model data available than for that with F2x/λ values and more scenario combinations to compare .
The slope t and r squared values in the table show a very strong correlation for the CMIP5 and CMIP6 Future scenarios between the GMST change and the climate sensitivity parameter .
The Historical scenarios for CMIP5 and CMIP6 were expected to show little correlation for these relationships @ @ @ @ @ @ @ R is abbreviated for RCP and the 2 numbers following are the forcing change goal for that scenario , i.e .
R45 stands for RCP4.5 .
The R designations are carried over from CMIP5 to CMIP6 .
S70 is unique to CMIP6 and has full designation as SSP3 7.0 .
The results in Table 3 are from 1000 simulations of the models referred to as Reproduction Models that were constructed to reproduce the regression results of the CMIP5 and CMIP6 scenario ensembles in Table 1 .
The simulation models for regression used the @ @ @ @ @ @ @ the independent and dependent variables , respectively : X= ( ΔR/F2x ) *F2x [ i ] /λ [ i ] , where X is the metric to which the individual model GMST change in the scenario should correlate without a variable negative forcing altering the relationship .
Y= ( ΔR/F2x ) *F2x [ i ] /λ [ i ] +rnorm ( n=1 , mean=K*Diff [ i ] , sd=SD ) , where Y is the simulated change in the individual model GMST over the scenario time period .
In the equations , ΔR @ @ @ @ @ @ @ from ΔR=ΔF-ΔN with ΔF being the change in forcing and ΔN being the TOA radiative imbalance .
These variables derive from the global energy budget ΔT= ( ΔF-ΔN ) /λ , where λ is the climate feedback parameter .
The value used for ΔN is the mean for ΔN for the scenario model ensemble and was taken for CMIP5 from KNMI Climate Explore ( reference 16 ) and for CMIP6 was taken from ESGF Node of DKRZ ( reference 17 ) .
The values for ΔF changes and the aerosol/cloud forcing changes used @ @ @ @ @ @ @ from pik-potsdam ( reference 18 ) , except for the special case of ssp 370 where the aerosol/cloud forcing changes were taken from Fiedler ( reference 19 ) .
F2x is the mean radiative forcing for a doubling of atmospheric CO2 concentration for the scenario ensemble being considered .
F2x [ i ] is the F2x for the individual model in the scenario .
λ [ i ] is the individual model feedback parameter .
Diff is the difference of the individual model feedback parameter from the mean of the scenario model feedback @ @ @ @ @ @ @ parameter that determines how much the individual model feedback parameter is compensated in moderating the Historical ΔT .
There have been a number of papers in the climate science literature to justify assuming that the other tunable Reproduction model parameter , which is the standard deviation , SD , in the random normal function that adds noise to the Y variable , can be considered at least partially attributable to the different choices available to the modelers of the individual models analyzed here .
The tendency of the models to compensate the GMST @ @ @ @ @ @ @ individual model climate sensitivities in bringing the results more in line with the observed GMST changes points to modelers decisions adding to overall uncertainty of the model results .
The structure variation from model to model could also determine the bounds of the potential influence of the modelers ’ decisions in this matter .
I have attempted to categorize the Reproduction model by searching the literature for similar examples .
The best examples found were presented in one paper ( reference 24 ) that dealt with stochastic and endogenous decision dependent uncertainty .
@ @ @ @ @ @ @ uncertainty in these models as : “ A problem is classified as having endogenous uncertainty when decisions that are part of the problem to be solved , influences the uncertainty of parameters that are also part of the problem.
” Table 2 below lists the parameter values used in the Reproduction Model .
The fixed parameters were used at values noted previously and taken from the literature and applied to the scenarios as reasonable weights for increasing the total net forcing in the scenario progression from Historical to R85 .
The tunable noise @ @ @ @ @ @ @ values for CMIP5 and CMIP6 but with the same values across the CMIP scenarios .
The tunable parameter , K , is the crux of this entire analysis and is the essential ingredient that causes most of the disconnection between the Historical and Future scenarios and at the same time closely reproduces the actual model results .
The negative K for the Historical period , that is multiplied by the difference of the individual model feedback parameter , λ , and the scenario ensemble mean of λ , produces a more negative or @ @ @ @ @ @ @ change , ΔT , for feedback parameters by the amount they deviate from the mean .
The future scenarios with the exception of S70 , have positive K values that , instead of compensating for feedback differences , enhances them .
The ratios of the K parameter for the scenarios were used in this analysis based on what was found in literature searches .
In other words for CMIP5 , where the Historical K is -0.30 and the Future K is approximately half that of the Historical but positive , the change in @ @ @ @ @ @ @ effects in the Historical period became nearly two times more negative than the forcing in the Future period became less negative .
Only the magnitude of K was tuned , but not the ratio between Historical and Future scenarios .
The Reproductive Models reproduce closely the results obtained from the CMIP5 and CMIP6 scenario ensembles of models even though fitting with the tunable parameters for this analysis was by no means exhaustive , but rather carried out with a few subjective iterations .
Table 4 contains the results for the Reproduction Models without @ @ @ @ @ @ @ model ΔT for the differing individual model feedback parameters .
The scenario model ensemble correlations are affected in this case by the differing scale for net Forcing change and the SD noise factor .
It can be seen from the table that the median probability correlations for both the CMIP5 and CMIP6 models of the Historical ensemble for ΔT versus F2x/λ and scenario ΔT versus ΔT have become significant and that those for the future scenarios remain significant and with slope t and r squared values generally not as large as those with @ @ @ @ @ @ @ are fully consistent with those in Rotstayn and Collier ( reference 25 ) who found that the TCR values for 14 CMIP5 models had no significant correlation with the GMST change in the Historical period ( 1860-2000 ) while in the same period the negative aerosol forcing change correlated with the GMST change with r value over 0.90 .
In other words the negative forcing that varied from model to model was changing what otherwise should have been a significant correlation between TCR ( and ECS ) and the change in GMST in @ @ @ @ @ @ @ two papers published where the individual model Historical aerosol forcing for some of the CMIP5 and CMIP6 models using a fixed SST and ocean ice covering are determined .
These data provided an opportunity for the analysis here to attempt to correct the Historical GMST changes for aerosol forcing and in turn regress the adjusted GMST change against F2x/λ .
There were only 12 models for these regressions but the results in Table 7 show that the adjusted GMST changes become very significant and have reasonably high r squared values .
The comparison @ @ @ @ @ @ @ between the unadjusted and adjusted Historical GMST changes shows most definitely and directly that the aerosol forcing applied in quantitatively different amounts to the individual models is the determining factor in disconnecting the model GMST changes from the model ’ s climate sensitivity .
The adjusted GMST change was derived using the following adjustment equation : ΔTcor= ΔTHist- ( F2x ( i ) /F2xmean ) * ( ( 0.65*ΔFaer ( i ) ) /λ ( i ) ) ) where ΔTcor is the individual model GMST change corrected for the individual model aerosol @ @ @ @ @ @ @ individual model GMST change , F2x ( i ) is the individual model forcing for a doubling of the atmospheric CO2 concentration , F2xmean is the ensemble mean of F2x , ΔFaer ( i ) is the Historical individual model aerosol forcing and λ ( i ) is the individual model feedback parameter .
The values of ΔFaer and ΔNaer were combined under the assumption that these two variables track one another Table 8 shows the OLS regression for CMIP5 and CMIP6 scenarios for all scenario combinations of ΔN versus ΔN .
Though @ @ @ @ @ @ @ into account any ΔN differences between the Historical and Future scenarios , the results are presented in the table to show another disconnection between the Historical and Future periods .
While the Zelinka paper ( reference 27 ) indicates that individual CMIP6 models could have Historical GMST compensation for differences in climate sensitivity from ΔN that is independent of the negative aerosol compensation , it is judged that the disconnect presented here between the ΔN for the Historical and Future periods is directly related to the negative aerosol compensation disconnection .
The aerosol @ @ @ @ @ @ @ would have a corresponding ΔN component and thus if there is a Historical and Future disconnection due to aerosol there should be a corresponding one for ΔN .
For CMIP5 scenarios the Historical to Future correlations are significantly negative for ΔN while for Future to Future scenarios the correlations are positive and high for ΔN – as they were for ΔT .
For CMIP6 scenarios the results are much the same as for CMIP5 except that the Historical to Future correlations while tending towards negative are not significant .
In this analysis it @ @ @ @ @ @ @ significant disconnection between the CMIP5 and CMIP6 Historical and Future period scenarios when considering the relationship between the individual model climate sensitivities and GMST values .
The correlations of changes in model GMST for scenario to scenario ensembles and ensemble scenario GMST changes to F2x/λ provide a means for analyzing the degree of disconnection between the Historical to Future scenarios and connection for Future to Future scenarios .
As an aside , using F2x/λ as a simplified version of the energy budget and obtaining high correlations with GMST changes in the Future periods @ @ @ @ @ @ @ value for these models where model to model variations in negative aerosol/cloud forcing are not an overwhelming factor .
The model ensemble correlations for both CMIP5 and CMIP6 Historical and Future scenarios can be closely reproduced with a simple model ( Reconstruction model ) .
This model accounts for negative aerosol/cloud forcing that can compensate GMST changes for differences in individual model climate sensitivities in the Historical period while allowing carry-over that forcing effect into the Future period scenarios and maintaining the higher scenario correlations in that period .
The Reproduction model can @ @ @ @ @ @ @ to Future disconnection that is consistent in time and forcing .
Added evidence favoring the Reproduction model comes from the result of using the model without the λ compensation factor and the observation that without inclusion of that factor there are significant changes in the Historical period correlations ( higher ) and smaller but noticeable ones ( lower ) in the Future period scenarios .
The results from adjusting the GMST changes in the Historical period for aerosol forcing adds more direct evidence that the aerosol forcing is the primary cause of the @ @ @ @ @ @ @ is well aligned with the Reproduction model results .
This added evidence negates the alternative explanation that the disconnection is the result of a scaling factor for a smaller ΔR in the Historical period .
It also shows that the negative aerosol/cloud forcing carried over from the Historical periods to the Future periods increases the leverage that climate sensitivity has on GMST in the Future periods and thus increases Future correlations of ΔT to F2x/λ .
It should be noted that the 95 % confidence intervals for the Reproduction model simulations for the @ @ @ @ @ @ @ cases to show statistically significant slopes and correlations ( but with a low probability ) in the 1000 regressions carried out .
That interval is related to the size of the tunable parameter of standard deviation .
That standard deviation has to include the variations in how the models and modelers handle the variable application of the negative aerosol/cloud forcing and that variation might well be truncated by some common sense judgments on selections .
The Reproduction model provides a wide range of choices but the modelers of individual models as a group @ @ @ @ @ @ @ near the middle of the distribution of choices and that appears to be the case .
What is the import of the disconnection of the ensembles of CMIP5 and CMIP6 models used in Historical and Future period scenarios ?
The process of using the Historical period model results for the climate variable of GMST change and how closely it matches the observed changes as a test of its credibility and capability of predicting future GMST changes becomes questionable if model selections of parameters and processes were aimed at better reproducing the observed GMST @ @ @ @ @ @ @ to the resulting negative aerosol/cloud forcing and potential for compensating for models with higher ( and lower ) climate sensitivities .
Alternatively if increasingly more negative aerosol/cloud forcing in the Historical period is handled as a natural feature of the model and does not get used either intentionally or unintentionally to moderate GMST changes , and further , if the Future period sees the aerosol/cloud forcing getting less negative , it could be supposed that a model with a higher climate sensitivity could reasonably well reproduce the observed GMST changes .
If all @ @ @ @ @ @ @ and Future changes in the negative aerosol/cloud forcing the correlations performed in this analysis would not show the disconnections that were found .
That means that only some – or none – of the models could be getting the negative forcing reasonably correct .
The finding of this analysis that the ΔN values for the ensemble Historical and Future periods do not correlate or are anti-correlated adds another independent means of the disconnection of the two periods .
As stated previously the ΔN data while being an independent source of data it is @ @ @ @ @ @ @ effect .
The only practical way out of this dilemma in my view is to find , or at least keep looking for , a more precise method of determining the observed climate sensitivity with narrowed confidence intervals .
The observed variables with the largest uncertainty required for the energy budget to estimate the observed climate sensitivity are the aerosol/cloud forcing and ΔN .
It is these variables that are also available in the climate models to compensate GMST Historical changes for variable climate sensitivities .
It is therefore those two forcing entities @ @ @ @ @ @ @ and the models .
The use of more complex and informed models that include endogenous decision uncertainty for modeling selection leading to variations in negative forcing between individual models in the Historical period could provide some needed insights in this area of climate science .

by Kenneth Fritsch
Abstract. An analysis is presented of he disconnection between the CMIP5 and CMIP6 Historical and Future periods when considering the relationship of the individual model GMST changes and the climate sensitivity. I have included a simple model that can account for the period disconnection using the negative forcing of aerosol/cloud effects in the Historical period that is carried forward into the Future period. I attribute some of the uncertainty in simulations of this simple model to endogenous model decision (selection) uncertainty that leads to variations in the changes of the negative forcing in the Historical period carried forward into the Future period.
Introduction:
There have been a number of references in the climate science literature (references 1 through10) concerning the disconnection between the change in the global mean surface temperature (GMST) in the Historical period (used in this analysis the time period 1850 or 1861 to 2014) and the climate sensitivity of models as measured by Equilibrium Climate Sensitivity (ECS) and Transient Climate Response (TCR) for individual CMIP5 and CMIP6 climate models. That disconnection has been made more obvious by the CMIP6 ensemble of models having higher climate sensitivities than that in the CMIP5 ensemble and yet having the near same GMST trends in the Historical period (references 11 and 12).
While there have been proposed explanations for this disconnection in the literature, mainly pointing to negative aerosol and cloud related forcing in the Historical period as probable causes, there has been neither an analysis of how this effect would carry over into the Future period (used in this analysis as the 2015-2100 time period) for individual models nor a direct comparison of the models’ GMST changes in Historical and Future periods. A theme in some of these papers suggests that direct model tuning of the Historical period warming does not account for the forcing compensation for differing model climate sensitivities but rather that it can be produced by selection from a range of parameter processes that can yield overall credible results and in turn a range of credible GMST trends. If the disconnection stems from a strictly structural difference between individual models, it would not change the concern for the models capability of reproducing the Historical GMST change translating to a capability to predict Future period GMST changes.
In this analysis I have used the energy budget equation, ΔT=(ΔF-ΔN)/λ , where ΔT is the GMST change, ΔF is the forcing change, ΔN is the change in TOA radiative imbalance and λ is the climate feedback parameter for an individual model. This relationship is further simplified by assuming that the quantity ΔF-ΔN in a correctly modeled form should be near constant for all models in a given scenario and time period and can be replaced by F2x which is the individual model radiative forcing from a doubling of atmospheric CO2 concentration. F2x/ λ is equivalent to ECS but for the purposes of this analysis the term F2x/λ will be used since it better denotes the simplifying relationship it has to the energy budget.
This analysis uses OLS regression of ΔT versus F2x/λ for individual models for a given scenario and time period and ΔT versus ΔT for individual models for scenario versus scenario. The analysis covers the CMIP5 and CMIP6 Historical and representative Future period scenarios. Also included in the analysis, as a possible contributing factor in the Historical to Future scenario disconnections, were the results of OLS regression of ΔN versus ΔN for all scenario combinations. The critical results derived from all these regressions were the slope t values and r squared values.
It was obvious from the start of this analysis that Future scenario ensembles, where the forcing from greenhouse gases (GHG) is dominating over negative aerosol forcing and noise, should have more significant slope t and r squared values than those derived from the Historical period. In this analysis the expectations of disconnections of the Historical ensemble results from those of the Future ensembles and how this could occur facilitated the construction of simple simulation scenario models (hereafter in this article referred to as Reproduction Models) that closely reproduce and provide a potential explanation of the differing model scenario results. I posted a previous thread here at Climate etc. (reference 13) on the disconnection between the Historical Future scenarios for CMIP5 models. That analysis did not provide a Reproduction like model showing how a negative forcing can compensate the climate sensitivities in the Historical period and be carried over into the Future period without materially reducing the correlations there. I attempt to provide that missing step in this analysis.
Methods:
All time series changes in climate variables in this analysis were determined using the empirical CEEMDAN method of extracting a secular trend from periodical oscillation and noise components (references 14 and 15). This method avoids having to limit the period selections to those where the non-trend components are not thought to bias the variable change. It also allows the change to be determined for the entire time period selected.
The individual model GMST and TOA radiation data and the corresponding Pre-Industrial Controls (PIC) for this analysis were taken for the CMIP5 scenarios from KNMI Climate Explorer website (reference 16) and for CMIP6 from the ESGF Node at DKRZ (reference 17). The forcing data for CMIP5 were taken from Meinshausen (reference 18) and that for CMIP6 were from Fiedler (reference 19). The λ and F2x data were taken from Gregory and Forster (references 20 and 21) for CMIP5 and from Femke (reference 22) for CMIP6.
The PIC series were tested for statistically significant trends and if none were found no adjustments were made to corresponding GMST and TOA series. There appeared no good reason to subtract non significant components (noise) from the series of interest. A with/without adjustment comparison of the final series changes over the period of interest showed no or very small differences relative to the series changes.
OLS regression was chosen for this analysis after comparing results for the slope and slope t values with Deming regressions. The small differences in the slope values for these two types of regression showed that the noise in the independent variable was not sufficient to require use of a version of Total Least Square regression. The r squared values for the regressions are reported with and without outliers along with the number of outliers found. These outliers were determined using Cook’s Distance criteria of greater than 4 times the mean of the Cook’s Distance for all the model results (reference 23). No effort was made to analyze the outliers since the motive for detecting them was not to subjectively exclude possible outliers but rather show how almost all models fit the regression line and allow comparison where all data points are used. The residuals from the OLS regressions were tested for auto correlation and fitting to a normal distribution. Based on those tests, no adjustments to the regression results were required.
Results and Discussion:
Table 1 shows the OLS regression results for the CMIP5 scenarios of Historical, RCP 4.5, RCP 6.0 and RCP 8.5 and for CMIP6 scenarios of Historical, SSP3 7.0 and the CMIP6 renditions of the CMIP5 RCP 4.5 and 8.5 scenarios. The ΔT versus ΔT regression for all scenario combinations was included with that for ΔT versus F2x/λ even though differences between the Historical and Future scenarios were expected to produce similar results with both regressions. With ΔT there were more model data available than for that with F2x/λ values and more scenario combinations to compare. The slope t and r squared values in the table show a very strong correlation for the CMIP5 and CMIP6 Future scenarios between the GMST change and the climate sensitivity parameter. The Historical scenarios for CMIP5 and CMIP6 were expected to show little correlation for these relationships and in fact show no correlation.
R is abbreviated for RCP and the 2 numbers following are the forcing change goal for that scenario, i.e. R45 stands for RCP4.5. The R designations are carried over from CMIP5 to CMIP6. S70 is unique to CMIP6 and has full designation as SSP3 7.0.
The results in Table 3 are from 1000 simulations of the models referred to as Reproduction Models that were constructed to reproduce the regression results of the CMIP5 and CMIP6 scenario ensembles in Table 1. The simulation models for regression used the following equations for X and Y, the independent and dependent variables, respectively:
X=(ΔR/F2x)*F2x[i]/λ[i], where X is the metric to which the individual model GMST change in the scenario should correlate without a variable negative forcing altering the relationship.
Y=(ΔR/F2x)*F2x[i]/λ[i]+rnorm(n=1, mean=K*Diff[i],sd=SD), where Y is the simulated change in the individual model GMST over the scenario time period.
In the equations, ΔR is the change in net outgoing radiation from ΔR=ΔF-ΔN with ΔF being the change in forcing and ΔN being the TOA radiative imbalance. These variables derive from the global energy budget ΔT=(ΔF-ΔN)/λ, where λ is the climate feedback parameter. The value used for ΔN is the mean for ΔN for the scenario model ensemble and was taken for CMIP5 from KNMI Climate Explore (reference 16) and for CMIP6 was taken from ESGF Node of DKRZ (reference 17). The values for ΔF changes and the aerosol/cloud forcing changes used for determining the K values were taken from pik-potsdam (reference 18), except for the special case of ssp 370 where the aerosol/cloud forcing changes were taken from Fiedler (reference 19). F2x is the mean radiative forcing for a doubling of atmospheric CO2 concentration for the scenario ensemble being considered. F2x[i] is the F2x for the individual model in the scenario. λ[i] is the individual model feedback parameter. Diff is the difference of the individual model feedback parameter from the mean of the scenario model feedback parameters. K is a tunable model parameter that determines how much the individual model feedback parameter is compensated in moderating the Historical ΔT.
There have been a number of papers in the climate science literature to justify assuming that the other tunable Reproduction model parameter, which is the standard deviation, SD, in the random normal function that adds noise to the Y variable, can be considered at least partially attributable to the different choices available to the modelers of the individual models analyzed here. The tendency of the models to compensate the GMST changes in the Historical period for varying individual model climate sensitivities in bringing the results more in line with the observed GMST changes points to modelers decisions adding to overall uncertainty of the model results. The structure variation from model to model could also determine the bounds of the potential influence of the modelers’ decisions in this matter. I have attempted to categorize the Reproduction model by searching the literature for similar examples. The best examples found were presented in one paper (reference 24) that dealt with stochastic and endogenous decision dependent uncertainty. A quote from reference 24 describes endogenous uncertainty in these models as: “A problem is classified as having endogenous uncertainty when decisions that are part of the problem to be solved, influences the uncertainty of parameters that are also part of the problem.”
Table 2 below lists the parameter values used in the Reproduction Model.
The fixed parameters were used at values noted previously and taken from the literature and applied to the scenarios as reasonable weights for increasing the total net forcing in the scenario progression from Historical to R85.
The tunable noise parameter of SD was applied at different values for CMIP5 and CMIP6 but with the same values across the CMIP scenarios. The tunable parameter, K, is the crux of this entire analysis and is the essential ingredient that causes most of the disconnection between the Historical and Future scenarios and at the same time closely reproduces the actual model results. The negative K for the Historical period, that is multiplied by the difference of the individual model feedback parameter, λ, and the scenario ensemble mean of λ, produces a more negative or positive forcing that compensates the resulting GMST change, ΔT, for feedback parameters by the amount they deviate from the mean. The future scenarios with the exception of S70, have positive K values that, instead of compensating for feedback differences, enhances them.
The ratios of the K parameter for the scenarios were used in this analysis based on what was found in literature searches. In other words for CMIP5, where the Historical K is -0.30 and the Future K is approximately half that of the Historical but positive, the change in the negative forcing from aerosol and cloud effects in the Historical period became nearly two times more negative than the forcing in the Future period became less negative. Only the magnitude of K was tuned, but not the ratio between Historical and Future scenarios.
The Reproductive Models reproduce closely the results obtained from the CMIP5 and CMIP6 scenario ensembles of models even though fitting with the tunable parameters for this analysis was by no means exhaustive, but rather carried out with a few subjective iterations.
Table 4 contains the results for the Reproduction Models without the factor that compensates the individual Historical model ΔT for the differing individual model feedback parameters. The scenario model ensemble correlations are affected in this case by the differing scale for net Forcing change and the SD noise factor. It can be seen from the table that the median probability correlations for both the CMIP5 and CMIP6 models of the Historical ensemble for ΔT versus F2x/λ and scenario ΔT versus ΔT have become significant and that those for the future scenarios remain significant and with slope t and r squared values generally not as large as those with the compensating factor.
The findings here are fully consistent with those in Rotstayn and Collier (reference 25) who found that the TCR values for 14 CMIP5 models had no significant correlation with the GMST change in the Historical period (1860-2000) while in the same period the negative aerosol forcing change correlated with the GMST change with r value over 0.90. In other words the negative forcing that varied from model to model was changing what otherwise should have been a significant correlation between TCR (and ECS) and the change in GMST in the Historical period.
There have been two papers published where the individual model Historical aerosol forcing for some of the CMIP5 and CMIP6 models using a fixed SST and ocean ice covering are determined. These data provided an opportunity for the analysis here to attempt to correct the Historical GMST changes for aerosol forcing and in turn regress the adjusted GMST change against F2x/λ. There were only 12 models for these regressions but the results in Table 7 show that the adjusted GMST changes become very significant and have reasonably high r squared values. The comparison of the t and r squared values between the unadjusted and adjusted Historical GMST changes shows most definitely and directly that the aerosol forcing applied in quantitatively different amounts to the individual models is the determining factor in disconnecting the model GMST changes from the model’s climate sensitivity.
The adjusted GMST change was derived using the following adjustment equation:
ΔTcor= ΔTHist- (F2x(i)/F2xmean)*((0.65*ΔFaer(i))/λ(i))) where ΔTcor is the individual model GMST change corrected for the individual model aerosol forcing, ΔTHist is the uncorrected derived individual model GMST change, F2x(i) is the individual model forcing for a doubling of the atmospheric CO2 concentration, F2xmean is the ensemble mean of F2x, ΔFaer(i) is the Historical individual model aerosol forcing and λ(i) is the individual model feedback parameter. The values of ΔFaer and ΔNaer were combined under the assumption that these two variables track one another
Table 8 shows the OLS regression for CMIP5 and CMIP6 scenarios for all scenario combinations of ΔN versus ΔN.
Though the Reproduction Models did not directly take into account any ΔN differences between the Historical and Future scenarios, the results are presented in the table to show another disconnection between the Historical and Future periods. While the Zelinka paper (reference 27) indicates that individual CMIP6 models could have Historical GMST compensation for differences in climate sensitivity from ΔN that is independent of the negative aerosol compensation, it is judged that the disconnect presented here between the ΔN for the Historical and Future periods is directly related to the negative aerosol compensation disconnection. The aerosol component and its effect on GMST change would have a corresponding ΔN component and thus if there is a Historical and Future disconnection due to aerosol there should be a corresponding one for ΔN. For CMIP5 scenarios the Historical to Future correlations are significantly negative for ΔN while for Future to Future scenarios the correlations are positive and high for ΔN – as they were for ΔT. For CMIP6 scenarios the results are much the same as for CMIP5 except that the Historical to Future correlations while tending towards negative are not significant.
Conclusions:
In this analysis it was shown that there is a statistically significant disconnection between the CMIP5 and CMIP6 Historical and Future period scenarios when considering the relationship between the individual model climate sensitivities and GMST values. The correlations of changes in model GMST for scenario to scenario ensembles and ensemble scenario GMST changes to F2x/λ provide a means for analyzing the degree of disconnection between the Historical to Future scenarios and connection for Future to Future scenarios.
As an aside, using F2x/λ as a simplified version of the energy budget and obtaining high correlations with GMST changes in the Future periods indicates that ΔR is a fairly constant value for these models where model to model variations in negative aerosol/cloud forcing are not an overwhelming factor.
The model ensemble correlations for both CMIP5 and CMIP6 Historical and Future scenarios can be closely reproduced with a simple model (Reconstruction model). This model accounts for negative aerosol/cloud forcing that can compensate GMST changes for differences in individual model climate sensitivities in the Historical period while allowing carry-over that forcing effect into the Future period scenarios and maintaining the higher scenario correlations in that period. The Reproduction model can provide a probable explanation for the Historical to Future disconnection that is consistent in time and forcing. Added evidence favoring the Reproduction model comes from the result of using the model without the λ compensation factor and the observation that without inclusion of that factor there are significant changes in the Historical period correlations(higher) and smaller but noticeable ones(lower) in the Future period scenarios. The results from adjusting the GMST changes in the Historical period for aerosol forcing adds more direct evidence that the aerosol forcing is the primary cause of the Historical GMST to climate sensitivity disconnection and is well aligned with the Reproduction model results. This added evidence negates the alternative explanation that the disconnection is the result of a scaling factor for a smaller ΔR in the Historical period. It also shows that the negative aerosol/cloud forcing carried over from the Historical periods to the Future periods increases the leverage that climate sensitivity has on GMST in the Future periods and thus increases Future correlations of ΔT to F2x/λ.
It should be noted that the 95% confidence intervals for the Reproduction model simulations for the Historical Period are sufficiently wide in some cases to show statistically significant slopes and correlations (but with a low probability) in the 1000 regressions carried out. That interval is related to the size of the tunable parameter of standard deviation. That standard deviation has to include the variations in how the models and modelers handle the variable application of the negative aerosol/cloud forcing and that variation might well be truncated by some common sense judgments on selections. The Reproduction model provides a wide range of choices but the modelers of individual models as a group could very well have independently selected from near the middle of the distribution of choices and that appears to be the case.
What is the import of the disconnection of the ensembles of CMIP5 and CMIP6 models used in Historical and Future period scenarios? The process of using the Historical period model results for the climate variable of GMST change and how closely it matches the observed changes as a test of its credibility and capability of predicting future GMST changes becomes questionable if model selections of parameters and processes were aimed at better reproducing the observed GMST changes and more specifically those choices related to the resulting negative aerosol/cloud forcing and potential for compensating for models with higher (and lower) climate sensitivities.
Alternatively if increasingly more negative aerosol/cloud forcing in the Historical period is handled as a natural feature of the model and does not get used either intentionally or unintentionally to moderate GMST changes, and further, if the Future period sees the aerosol/cloud forcing getting less negative, it could be supposed that a model with a higher climate sensitivity could reasonably well reproduce the observed GMST changes. If all the models had nearly the same Historical and Future changes in the negative aerosol/cloud forcing the correlations performed in this analysis would not show the disconnections that were found. That means that only some – or none – of the models could be getting the negative forcing reasonably correct.
The finding of this analysis that the ΔN values for the ensemble Historical and Future periods do not correlate or are anti-correlated adds another independent means of the disconnection of the two periods. As stated previously the ΔN data while being an independent source of data it is not necessarily independent of the main aerosol effect.
The only practical way out of this dilemma in my view is to find, or at least keep looking for, a more precise method of determining the observed climate sensitivity with narrowed confidence intervals. The observed variables with the largest uncertainty required for the energy budget to estimate the observed climate sensitivity are the aerosol/cloud forcing and ΔN. It is these variables that are also available in the climate models to compensate GMST Historical changes for variable climate sensitivities. It is therefore those two forcing entities that need attention, in the observations and the models.
The use of more complex and informed models that include endogenous decision uncertainty for modeling selection leading to variations in negative forcing between individual models in the Historical period could provide some needed insights in this area of climate science.
References [ References]
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