By Kenneth Richard on 6. December 2018
Many new scientific papers affirm climate model results conflict with one another, diverge from observations, and aren’t fully rooted in established physics.
Climate models are predicated on the assumption that greenhouse gases exert fundamental control on the Earth’s climate system. That’s why for decades it’s been predicted that disaster will befall the planet as a consequence of rising CO2 emissions.
And yet contrary to how they are popularly portrayed, climate models do not fully employ the laws of physics in their representations (Essex and Tsonis, 2018). This is likely why climate model outputs are (a) often widely different from one another and (b) frequently diverge from real-world observations.
We Lack Understanding of Climate Mechanisms
In contrast with governmental (the United Nations’) manufactured framework of certainty, scientists are increasingly suggesting we have yet to adequately understand fundamental processes and mechanisms in the Earth’s climate system.
“[W]e can build and run complex models of the Earth system, but we do not have adequate enough understanding of the processes and mechanisms to be able to quantitatively evaluate the predictions and projections they produce, or to understand why different models give different answers.” (Collins et al. 2018)
“[C]limate changes in polar areas remain difficult to predict, which indicates that the underlying mechanisms of polar amplification remain uncertain and debatable.” (Ding et al., 2018)
Furthermore, because climate models aren’t subjected to the “hard” science standard of falsification, they are necessarily presented to the non-skeptical public as unfalsifiable. In other words, when climate models don’t agree with real-world observations, they aren’t assumed to be wrong, or worthy of disposal. Instead, they are suggested to merely need a little re-tuning.
The refusal to discard climate models that conflict with observations is apparently rooted in politics. Kundzewicz et al. (2018) point out that the “hard” science standard that says results should be quantitatively validated with a measured degree of certainty before formulating policy initiatives is deemed “unrealistic and counterproductive” today. That’s why climate modeling thrives in the modern “soft” political world – a realm where the rigors of observation and falsification — the scientific method — need not apply.
“[I]n the past, science was assumed to provide ‘hard’ results in quantitative form, in contrast to ‘soft’ determinants of politics, that were interest-driven and value-laden. Yet, the traditional assumption of the certainty of scientific information is now recognized as unrealistic and counterproductive.” (Kundzewicz et al., 2018)
Climate Models Don’t Agree With Reality
Problematically, even when they are re-tuned, climate models still yield widely divergent outputs both from one another and compared to observational evidence.
Many new scientific papers have been published in recent months that document the failure of climate models to simulate the Earth’s climate. A sampling of 10 peer-reviewed papers from 2018 are highlighted below.
In several cases, scientists have reported that none of the modern-day climate model results are consistent with real-world observations. In some cases the models yield opposite results (i.e., warming instead of cooling, rising instead of falling, etc.).
It is increasingly being recognized that climate models “not only don’t agree with each other when it comes to dynamics, they also don’t agree with reality” (Essex and Tsonis, 2018).
• “Climate models do not and cannot employ known physics fully. Thus, they are falsified, a priori. Incomplete physics and the finite representation of computers can induce false instabilities.”
• “The standard model of physics, for example, is subject to falsification. If it fails to make correct predictions in controlled experiments, it is false. Projections are not good enough there. Even in astrophysics, models explain phenomena that are normally subject to falsification through broad questions asked about multiple occurrences of similar physical circumstances, even in highly data-starved contexts. What makes climate models fundamentally different is that they are presented as being unfalsifiable. Even when they deviate from actual observations, they are not superseded by a better competing model. Deviations simply invite some retuning. Moreover instead of replacement by better models retuning leads to all models becoming more alike.”
• “[A]re there propositions that contemporary models make, crucial to their own objectives, that are falsifiable? Is there any physical test possible that would force us to conclude that they are unable to achieve their own objectives, thus requiring a rethinking of basic assumptions? This paper addresses this question. But it is a question that cannot be comprehended in the face of many widely-held misconceptions about the direct meteorologically based projection modeling of climate. Foremost among these misconceptions is that climate models are full implementations of known, mature physics. This false conception can lead to the conclusion that falsification is irrelevant because models are simply an execution of previously known correct physics.”
• “The empirical nature of large climate models can be clearly seen in their diverse outputs. If they followed the laws of physics in their entirety, they would all produce the same results under the same conditions. But they do not. In a recent study, the Climate Model Inter-comparison Project phase 3 (CMIP3) models  were considered and a detailed comparison at the dynamics level, using an approach involving climate networks [Steinhaeuser and Tsonis, 2013] was performed. It was found that the models not only don’t agree with each other when it comes to dynamics, they also don’t agree with reality.”
• “Here there is a dynamical gap in our understanding. While we have conceptual models of how weather systems form and can predict their evolution over days to weeks, we do not have theories that can adequately explain the reasons for an extreme cold or warm, or wet or dry, winter at continental scales. More importantly, we do not have the ability to credibly predict such states.”
• “Likewise, we can build and run complex models of the Earth system, but we do not have adequate enough understanding of the processes and mechanisms to be able to quantitatively evaluate the predictions and projections they produce, or to understand why different models give different answers.
• “The global warming ‘hiatus’ provides an example of a climate event potentially related to inter-basin teleconnections. While decadal climate variations are expected, the magnitude of the recent event was unforeseen. A decadal period of intensified trade winds in the Pacific and cooler sea surface temperatures (SSTs) has been identified as a leading candidate mechanism for the global slowdown in warming.”
• “Climate models need to be improved before they can be effectively used for adaptation planning and design. Substantial reduction of the uncertainty range would require improvement of our understanding of processes implemented in models and using finer resolution of GCMs and RCMs. However, important uncertainties are unlikely to be eliminated or substantially reduced in near future (cf. Buytaert et al., 2010). Uncertainty in estimation of climate sensitivity (change of global mean temperature, corresponding to doubling atmospheric CO2 concentration) has not decreased considerably over last decades. Higher resolution of climate input for impact models requires downscaling (statistical or dynamic) of GCM outputs, adding further uncertainty.”
• “[C]limate models do not currently simulate the water cycle at sufficiently fine resolution for attribution of catchment-scale hydrological impacts to anthropogenic climate change. It is expected that climate models and impact models will become better integrated in the future.”
• “Calibration and validation of a hydrological model should be done before applying it for climate change impact assessment, to reduce the uncertainty of results. Yet, typically, global hydrological models are not calibrated and validated. … Model-based projections of climate change impact on water resources can largely differ. If this is the case, water managers cannot have confidence in an individual scenario or projection for the future. Then, no robust, quantitative, information can be delivered and adaptation procedures need to be developed which use identified projection ranges and uncertainty estimates. Moreover, there are important, nonclimatic, factors affecting future water resources.”
• “As noted by Funtowicz and Ravetz (1990), in the past, science was assumed to provide “hard” results in quantitative form, in contrast to “soft” determinants of politics, that were interest-driven and value-laden. Yet, the traditional assumption of the certainty of scientific information is now recognized as unrealistic and counterproductive. Policy-makers have to make “hard” decisions, choosing between conflicting options (with commitments and stakes being the primary focus), using “soft” scientific information that is bound with considerable uncertainty. Uncertainty has been policitized in that policy-makers have their own agendas that can include the manipulation of uncertainty. Parties in a policy debate may invoke uncertainty in their arguments selectively, for their own advantage.”
• “The representation of clouds over Greenland is a central concern for the models because clouds impact ice-sheet surface melt. We find that over Greenland, most of the models have insufficient cloud cover during summer. In addition, all models create too few non-opaque liquid containing clouds optically thin enough to let direct solar radiation reach the surface (-1% to -3.5% at the ground level). Some models create too few opaque clouds. In most climate models, the cloud properties biases identified over all Greenland also apply at Summit proving the value of the ground observatory in model evaluation.”
• “At Summit, climate models underestimate cloud radiative effect (CRE) at the surface, especially in summer. The primary driver of the summer CRE biases compared to observations is the underestimation of the cloud cover in summer (-46% to -21%), which leads to an underestimated longwave radiative warming effect (CRELW = -35.7 W m-2 to -13.6 W m-2 compared to the ground observations) and an underestimated shortwave cooling effect (CRESW = +1.5 W m-2 to +10.5 W m-2 compared to the ground observations). Overall, the simulated [modeled] clouds do not radiatively warm the surface as much as observed.”
• “Of particular importance, clouds can trigger surface melt over a large portion of the Greenland Ice Sheet (Bennartz et al. 2013; Solomon et al. 2017). Greenland surface melting increases non-linearly with increasing temperatures due to positive feedbacks between cloud microphysics, surface melting and surface albedo (Fettweis et al. 2013) and modulates the ice sheet mass balance (Van Tricht et al. 2016; Hofer et al. 2017).”
• “Every model included in this study underestimates the net cloud radiative surface warming in summer. … [O]nly few general circulation models are able to represent the surface of the Greenland ice sheet (Cullather et al. 2014). … Since the overall cloud radiative warming is underestimated in the models, we may expect an underestimate of Greenland surface melting. However, misrepresentation of clouds is not the only contributor to biases in the modeled surface melting.”
None of the climate models match the observations
• “[D]eviations of the model-simulated climate change from observations, such as a recent “pause” in global warming, have received considerable attention. Such decadal mismatches between model-simulated and observed climate trends are common throughout the twentieth century, and their causes are still poorly understood.”
• “While climate models exhibit various levels of decadal climate variability and some regional similarities to observations, none of the model simulations considered match the observed signal in terms of its magnitude, spatial patterns and their sequential time development. These results highlight a substantial degree of uncertainty in our interpretation of the observed climate change using current generation of climate models.”
• “In summary, there is marginal evidence for an emerging detectable anthropogenic contribution toward earlier WSCT [winter-spring center time] in parts of North America. The regions with strongest relative indication of an anthropogenic contribution in our analysis include: the north-central U.S. (Region 3); the mountainous western U.S./southwestern Canada (Region 1); and extreme northeastern U.S. and Canadian Maritimes (Region 6).”
• “However, in none of the regions examined do a majority of the nine CMIP5 models examined robustly support a detectable attribution of an earlier (decreasing) WSCT trend to anthropogenic forcing. At some level, the difficulty in detecting a climate change signal comes down to low signal to noise ratio (Ziegler et al. 2005). Apparently, for the variable at hand, the climate change influence is not very large compared to interannual/interdecadal variability noise.”
• “The fluctuation statistics of the observed sea-ice extent during the satellite era are compared with model output from CMIP5 models using a multifractal time series method. The two robust features of the observations are that on annual to biannual time scales the ice extent exhibits white noise structure, and there is a decadal scale trend associated with the decay of the ice cover.”
• “It is shown that (i) there is a large inter-model variability in the time scales extracted from the models, (ii) none of the models exhibits the decadal time scales found in the satellite observations, (iii) five of the 21 models [24%] examined exhibit the observed white noise structure, and (iv) the multi-model ensemble mean exhibits neither the observed white noise structure nor the observed decadal trend.”
• “Over the recent three decades sea surface temperate (SST) in the eastern equatorial Pacific has decreased, which helps reduce the rate of global warming. However, most CMIP5 model simulations with historical radiative forcing do not reproduce this Pacific La Niña-like cooling. Based on the assumption of ‘perfect’ models, previous studies have suggested that errors in simulated internal climate variations and/or external radiative forcing may cause the discrepancy between the multi-model simulations and the observation.”
• “Based on the total 126 realizations of the 38 CMIP5 model Historical simulations, the results show that none of the 126 model historical realizations reproduce the intensity of the observed eastern Pacific cooling (Fig. 1d) and only one simulation produces a weak cooling (−0.007 °C per decade).”
• “Recent changes in summer Greenland blocking captured by none of the CMIP5 models“
• “Given well-established connections between atmospheric pressure over the Greenland region and air temperature and precipitation extremes downstream … this brings into question the accuracy of simulated North Atlantic jet stream changes and resulting climatological anomalies … as well as of future projections of GrIS mass balance produced using global and regional climate models.”
• “The models underestimate the large decadal (2002–2014) trends in water storage relative to GRACE satellites, both decreasing trends related to human intervention and climate and increasing trends related primarily to climate variations. The poor agreement between models and GRACE underscores the challenges remaining for global models to capture human or climate impacts on global water storage trends.”
• “Increasing TWSA [total water storage anomalies] trends are found primarily in nonirrigated basins, mostly in humid regions, and may be related to climate variations. Models also underestimate median GRACE increasing trends (1.6–2.1 km3/y) by up to a factor of ∼8 in GHWRMs [global hydrological and water resource models] (0.3–0.6 km3/y).”
• “Underestimation of GRACE-derived TWSA increasing trends is much greater for LSMs [global land surface models], with four of the five LSMs [global land surface models] yielding opposite trends (i.e., median negative rather than positive trends).”
• “Increasing GRACE trends are also found in surrounding basins, with most models yielding negative trends. Models greatly underestimate the increasing trends in Africa, particularly in southern Africa.”
• “TWSA trends from GRACE in northeast Asia are generally increasing, but many models show decreasing trends, particularly in the Yenisei.”
• “[T]he magnitude of the estimated climate contribution to GMSL [global mean sea level] is twice that of the human contribution and opposite in sign.”
Posted in Climate Politics, Models |
39 responses to “Scientists: ‘Falsified’ Climate Models ‘Do Not Employ Known Physics Fully’…‘Don’t Agree With Reality’”
Beware the fake claim that the climate models are based on those used in meteorology, which are known to work (sort of), so move along, nothing to see here. That argument is false in many aspects because meteorology has never concerned itself with CHANGES in CO2, its models only work for the current level.
How does a doubling of CO2 affect clouds? Yes, physics-based models are used to get an answer, but there is no way for these models to be validated.
Climate models produce predictions that disagree with each other hugely. So, clearly, even if one of them is right, all the others must be wrong!
Yup, …which is why they average them together to get what they want us to believe is a credible result. Some “scientists” have no shame.
Oh, and just because all but that one are clearly wrong, that doesn’t make that one true. You know what they say about a broken clock.
Gus, this makes no sense in the context of models and model runs. Have you any idea what exactly you are arguing against here?
Seb, your reply makes clear you don’t understand what is real and what is guessed.
Sunsettommy, are you sure you understand what I wrote? 🙂
“If they followed the laws of physics in their entirety, they would all produce the same results under the same conditions.”
From chaos theory we know that even if we model the exact dynamical laws of a system, slight variations in initial conditions, which can never be known accurately enough, can give completely different end results.
Now add in important significant drivers like the sun, cosmic rays, volcanic activity, etc., which are variables that we have no ability to forecast. What you get is a “couple non-linear (stochastic) chaotic system,” which fact even the IPCC is aware of, and at one point acknowledged the impossibility of modeling.
Add in rounding errors in fixed precision real numbers and you get errors adding on top of errors until there is nothing but errors left.
But in their case that’s not a bug, it’s a feature.
The more noise, the more room for “creativity” in the signal – wink wink, nudge nudge.
Karl Popper is spinning in his grave about the unfalsifiabilty of climate “science”.
Just like Astrology, Popper would describe it as a “pseudoscience”.
And rightly so.
Oh come on, a theory is easily proven wrong. Why don’t you try to do that if climate science has it so obviously wrong in your view?
We have, numerous times.
You haven’t, not a single time.
Oh yes it has Seb Many times by many people.
Why don’t you practice what you preach?
@Don from OZ
“Why don’t you practice what you preach?”
Because, Don, SebH is a troll. They will NEVER admit when they are wrong, which is always. Their whole purpose is to distract and annoy be being as wrong as they can be. So, don’t expect to reason with him, because even when he knows you are correct he will not admit it.
I still think Pierre should limit the number (and length) of posts he should be allowed in a day. That might force him to be more economical in what he writes. And, no, I don’t think it will improve the quality of his nonsense, but at least we won’t be the only ones annoyed. 😉
Yonason, I am limiting myself, but this is a 1-to-many conversation. I reply to something and 5 of you guys reply to it with different nonsense. Should I leave those replies unanswered? I am trying to combine replies into a single comment already.
Oh, and your comment here is kind of childish. Why should I admit that I am wrong on this? You guys are arguing against climate science, not me. Prove climate science and the current theories about how stuff works wrong! You guys claim you already did, which is a testament to your imagination, nothing more 😉
Rest assured, I find you very annoying as well and seeing your replies is always distracting. Or do you think your reply here has anything to do with the thread you replied to? It’s just an ad hominem attack.
The sad part is that there are almost no instances where you are correct. It’s actually amazing that you don’t see the inconsistencies in your fantasy reality.
How does one prove a theory (actually, it hasn’t even reached the status of an hypothesis) wrong if the assumption that CO2 concentration changes cause heat changes in water has never been quantified or observed? It’s like asking someone to prove rising (or falling) CO2 caused the Permian Extinction.
@Dom from Oz
We don’t have to look far to see that the troll, SebH is lying. See my recent comment here (as well as Kenneth Richard’s response to it).
He has no shame, and can’t be trusted with anything. Even if he were to tell the truth about something you weren’t familiar with, how would you know?
@Hivemind 7. Dec 2018 at 2:58 AM
Yes. And as much evidence as there is against it,…
…tho$e who $till $upport it mu$t have really powerful incentive$ to to $o.
Climate Science hasn’t even reached theory stage yet. It’s still only an assortment of hypotheses.
And in the case of the claim that CO2 molecules concentrations are the determinant of ocean heat content changes, we don’t even have a theory…or even an hypothesis. Hypotheses are necessarily based upon real-world observations. And when it comes to quantifying the effect of CO2 molecule changes on water temperature changes, we have nothing of the kind.
So to “prove” a theory wrong, we need to first have an actual theory. And what do we have instead? A set of assumptions.
I guess in your world it is also an assumption that something falls towards a mass. We have never observed a football to fall onto Saturn, so is this an assumption when we claim that it would certainly fall? Why is it an assumption when you apply the laws of physics in climate science? Just because you don’t like or understand the implications?
I am guessing you have personally observed how cloud cover is the determinant of ocean heat content in recent history, right? 😉
So, are you saying the so called greenhouse effect doesn’t exist because you think it has never been observed? Or are you saying something else entirely? I don’t want to quote you incorrectly on this …
We aren’t talking about footballs. We aren’t talking about Saturn. No one is making a claim that a football would “fall onto Saturn”. But, of course, when you have nothing in terms of a germane argument, changing the topic is the “thing to do”.
If you’d read the article, you’d realize that the scientists are saying that the laws of physics are not fully employed in CO2-based climate models.
There are “many widely-held misconceptions about the direct meteorologically based projection modeling of climate. Foremost among these misconceptions is that climate models are full implementations of known, mature physics. This false conception…”
The laws of physics require real-world observations. We have no real-world observational evidence that quantifies the effect of CO2 concentration changes on water temperatures. How much change does an increase of 0.00001 (10 ppm) in CO2 concentration in water temperature? 0.0000001 K? 0.0000000000000000001 K? 0.00001 K? Do you know? Of course you don’t. We have no real-world observational evidence quantifying the effect of CO2 changes on water temperatures. Therefore, we are not employing physical laws when the claim is made that 100% of the climate changes since 1950 are human-caused.
Why would you guess I have “personally observed” cloud cover determining ocean heat content changes? Or are you (falsely) claiming I have written that?
I’ve not written that the “greenhouse effect doesn’t exist”. I’ve written (repeatedly) that we have no real-world observations that quantify the effect of CO2 changes on water temperature. Therefore, we don’t know to what extent changes in CO2 concentration affect the ocean heat content…and ultimately the climate. It’s assumption and surmise. That’s why it hasn’t even risen to the level of a theory…or hypothesis. Those require real-world observational evidence.
You can’t even be honest in a feigned attempt to appear honorable. You obviously have no problem at all quoting me incorrectly. You routinely make stuff up and claim that that’s what I’ve written or think.
Another quote from Popper “A theory of everything proves nothing”.
This is what climate “science” has become: unfalsifiable.
It’s colder, warmer, wetter, drier. Any change is “consistent with the theory”.
You might as well try and disprove the existence of God to the faithful.
Just like any movement of an object is consistent with the laws of physics? Your arguments roots in desperation, B&T. If you could actually show that climate science is nonsense and that what you blogscientists claim is what is really happening ™, you would have already done that. So far only meaningless words about how everything is too complex to ever fully understand from you guys. That’s what religious people generally say …
I’m sure the climate models would work a whole lot better if they included the ~60 year cycle and the modulation of cloud cover by the Sun. They don’t because if they did they’d lose their funding.
In turn this causes a wicked modelling problem, since they have to reduce the insolation effect of clouds, or the models wouldn’t fit last century temperatures. Then they have to increase the effect of aerosols to correct the cloud distortion. Which in turn means they don’t get rainfall right because aerosols and clouds together cause rain.
So distorted are these variables that the models become doubly useless, since the omission of two significant variables causes them to have to distort two more significant variables to make up for that, until the whole thing becomes a joke.
Unwinding all this distortion and adding in the real climate drivers would almost certainly make the models fit the temperature record AND give them some skill at forward projection.
But they can’t do that without collapsing the whole climate scam.
Ah yes, the good old “they are ignoring the real cause” argument. I find it fascinating that blog-comment-science has such a clear perception of reality while actual science iseems to be basically blind to the truth in your universe 🙂
My comment science is based on expertise in multiple regression (I have a published paper on it – which was actually audited by a stats consultancy, plus I’ve many more publications on other science topics), thermodynamics, data analysis plus extensive statistical and process modelling. My PhD is in chemistry and I’ve worked over 30 years in R&D. I’ve never bothered to count the number of R&D projects I’ve worked on, it’d be triple figures easily.
I’d preen at this point, but that would be just rubbing it in.
Seb, if you want to play the argumentum ab auctoritate game that is fine by me since you just lost.
It’s not just me either. Guys like Prof. Udupi Rao who was India’s pre-eminent scientist and head of their space program for many years, was a GCR astrophysicist. He determined a solar contribution to warming last century of just under 50%. Not CO2, which is harmless. Any wonder that India is building over a hundred coal fired power stations right now?
When will you people get the message that climate is a lot more complex than you think, and that CO2 is just one of many players? And not a large one, either.
The one playing this particular game is you and I think only the one playing it can and will always lose.
Yep, you are definitely the one playing …
Sure, harmless … *sigh*
What do you think? Might it have something to do with not being able to build renewables fast enough? Renewables are growing as fast as they can right now and each year we get better and better at building them adding more capacity to do so. As supply can’t match demand yet nations will continue to build other forms of electricity generation as well, even if they are many times as expensive (search for what new nuclear power plants currently getting build cost).
Another skeptic classic: “it’s more complex than you think” or too complex to ever find out what is really going on or some variation of that.
Are you even following anything that comes from climate science? Nobody is arguing that CO2 is the only player. Why this strawman?
You go on and continue to believe that you and your fellow skeptic friends are the only ones seeing the truth and climate science has it all wrong … I guess we should all listen to you guys and ignore the pros currently working in the field, right?
Says the very same person who has claimed that 100% of the overall temperature changes since 1950 are human-caused.
Back to basics DNCWTRT or if prefered DNFTT🤮☠️
“Actual science” must, as you say, be entirely blind. They didn´t even see the simplest solution: Solar irradiance on the disc, pir^2, distributed over the hemisphere, 2pir^2, through double shell volumes of a sphere, (4pir^3/3)^2:
This equation gives average temperature at 1 bar pressure on Venus, Earth and Mars. It is THE simplest solution possible, the ideal, optimized model of heat flow with no resistance, only geometrical limitations. And it gives the correct results.
The greenhouse theory is the most embarrasing mistake in the history of science. It´s worse than believing the Earth is flat, because even back then, in the flat Earth era, nobody was stupid enough to think that cold air warms hot solids.
NOTHING TO SEE HERE — MOVE ALONG.
WHAT SCIENCE IZ?
There is nothing wrong with warmist science. It just seems that way because, as warmists say, you skeptics “don’t know what science is…” And, if you are disturbed by the above article, they are correct. You don’t have a clue.
Well, fear not, skeptics. No longer will you wonder what they mean. Bob Carter has a short video to ‘splain it to youze all. So, without further ado, here’s what warmist science is, or more correctly has sadly become, and why.
Here’s some supplementary information on post-modern science.
…and one on what consequences to expect from it.
So, now you know what science iz, and that it shouldn’t make sense, because it’s not supposed to. You will no longer be troubled when it makes no sense to you. But, if you are normal, you will be ticked off.
[…] No Tricks Zone – Scientists: ‘Falsified’ Climate Models ‘Do Not Employ Known Physics Ful… […]
[…] Many new scientific papers affirm climate model results conflict with one another, diverge from observations, and aren’t fully rooted in established physics. (Source) […]
Climate models not only disagrees With each other, they also disagree seriously With measurements of real temperature on Earth.
To HIDE this, they never present their calculated actual temperature, but transform it into “anomalies” by subtracting the SIMULATED Reference temperature from a certain Reference period.
Thus, the modellers are able to hide that they predict completely wrong energy Levels in the athmosphere (and sea Surface).
And don’t forget that in order to get the “data” to compare to the models, they had to “adjust” the raw data…
…so there is more than just one level of deception.
See also here.
From the date you can see we’ve known about this for a long time. And from the specific location, Brisbane, we see that this data tampering isn’t restricted to the USA.
They also make up data for areas in which they have none. It sounds a lot like “ballot harvesting” to me, which is how Kalifornia stole the last election.
Wherever Leftists are in charge, we can’t expect the truth about anything.
[…] https://notrickszone.com/2018/12/06/scientists-falsified-climate-models-do-not-employ-known-physics-f… […]
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