Climate Models Shown Inaccurate -- Again

This time the climate models may be down for the count. Two new papers independently demonstrate the models' inaccuracy.


New confirmation that climate models overstate atmospheric warming

Posted on August 25, 2020 by curryja | 31 comments
by Ross McKitrick
Two new peer-reviewed papers from independent teams confirm that climate models overstate atmospheric warming and the problem has gotten worse over time, not better.
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Two new peer-reviewed papers from independent teams confirm that climate models overstate atmospheric warming and the problem has gotten worse over time, not better.

The papers are Mitchell et al. (2020) “The vertical profile of recent tropical temperature trends: Persistent model biases in the context of internal variability” Environmental Research Letters, and McKitrick and Christy (2020) “Pervasive warming bias in CMIP6 tropospheric layers” Earth and Space Science. John and I didn’t know about the Mitchell team’s work until after their paper came out, and they likewise didn’t know about ours.

Mitchell et al. look at the surface, troposphere and stratosphere over the tropics (20N to 20S). John and I look at the tropical and global lower- and mid- troposphere. Both papers test large samples of the latest generation (“Coupled Model Intercomparison Project version 6” or CMIP6) climate models, i.e. the ones being used for the next IPCC report, and compare model outputs to post-1979 observations. John and I were able to examine 38 models while Mitchell et al. looked at 48 models. The sheer number makes one wonder why so many are needed, if the science is settled. Both papers looked at “hindcasts,” which are reconstructions of recent historical temperatures in response to observed greenhouse gas emissions and other changes (e.g. aerosols and solar forcing). Across the two papers it emerges that the models overshoot historical warming from the near-surface through the upper troposphere, in the tropics and globally. . . .

The conclusion:


New confirmation that climate models overstate atmospheric warming

Posted on August 25, 2020 by curryja | 32 comments
by Ross McKitrick
Two new peer-reviewed papers from independent teams confirm that climate models overstate atmospheric warming and the problem has gotten worse over time, not better.
Continue reading →

Concluding remarks

"I get it that modeling the climate is incredibly difficult, and no one faults the scientific community for finding it a tough problem to solve. But we are all living with the consequences of climate modelers stubbornly using generation after generation of models that exhibit too much surface and tropospheric warming, in addition to running grossly exaggerated forcing scenarios (e.g. RCP8.5). Back in 2005 in the first report of the then-new US Climate Change Science Program, Karl et al. pointed to the exaggerated warming in the tropical troposphere as a “potentially serious inconsistency.” But rather than fixing it since then, modelers have made it worse. Mitchell et al. note that in addition to the wrong warming trends themselves, the biases have broader implications because “atmospheric circulation trends depend on latitudinal temperature gradients.” In other words when the models get the tropical troposphere wrong, it drives potential errors in many other features of the model atmosphere. Even if the original problem was confined to excess warming in the tropical mid-troposphere, it has now expanded into a more pervasive warm bias throughout the global troposphere.

If the discrepancies in the troposphere were evenly split across models between excess warming and cooling we could chalk it up to noise and uncertainty. But that is not the case: it’s all excess warming. CMIP5 models warmed too much over the sea surface and too much in the tropical troposphere. Now the CMIP6 models warm too much throughout the global lower- and mid-troposphere. That’s bias, not uncertainty, and until the modeling community finds a way to fix it, the economics and policy making communities are justified in assuming future warming projections are overstated, potentially by a great deal depending on the model."

It'd be nice to be an expert in the field, to be able to assess who's telling the truth with more detailed knowledge. *sigh*

Ah well. On the bright side, I don't believe the issue will ultimately be decided on whose science wins out, so I needn't burden myself.

COTO said:

It'd be nice to be an expert in the field, to be able to assess who's telling the truth with more detailed knowledge. *sigh*

Ah well. On the bright side, I don't believe the issue will ultimately be decided on whose science wins out, so I needn't burden myself.

Click to expand...

Ultimately, the data will prevail.

Here are the papers.

[FONT=&quot]McKitrick and Christy (2020) “Pervasive warming bias in CMIP6 tropospheric layers” Earth and Space Science.[/FONT]
[FONT=&quot]Mitchell et al. (2020) “The vertical profile of recent tropical temperature trends: Persistent model biases in the context of internal variability” Environmental Research Letters.[/FONT]

Jack Hays said:

Here are the papers.

[FONT="][URL="https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2020EA001281"]McKitrick and Christy (2020)[/URL] “Pervasive warming bias in CMIP6 tropospheric layers” Earth and Space Science.[/FONT]
[FONT="][URL="https://iopscience.iop.org/article/10.1088/1748-9326/ab9af7"]Mitchell et al. (2020)[/URL] “The vertical profile of recent tropical temperature trends: Persistent model biases in the context of internal variability” Environmental Research Letters.[/FONT]

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I think what a lot of people do not understand, is that a warming bias, in a model, is a systematic error,
the error accumulates as the model runs longer.

longview said:

I think what a lot of people do not understand, is that a warming bias, in a model, is a systematic error,
the error accumulates as the model runs longer.

Click to expand...

Because so much of the alarmist narrative depends on models, there is diminished incentive to correct the error.

Jack Hays said:

Because so much of the alarmist narrative depends on models, there is diminished incentive to correct the error.

Click to expand...

Perhaps, but the margin of error is small enough to give someone wanting to take an alarmist position cover.
The problem is that the minor errors accumulate to the point that extended predictions are meaningless.
Just like the cone of uncertainty of a hurricane is much larger as the time period expands in days,
the cone of uncertainty for a future climate expands.
Within a few years, a person can say that their prediction is within the range of uncertainty,
while not acknowledging that the range is large enough to accommodate almost any prediction.

CMIP6 Update
This essay extends the previously published evaluation of CMIP5 climate models to the predictive and physical reliability of CMIP6 global average air temperature projections.
Continue reading →

". . . Several conclusions follow.

First, CMIP6 models, like their antecedents, project air temperatures as a linear extrapolation of forcing.

Second, CMIP6 climate models, like their antecedents, make large scale simulation errors in cloud fraction.

Third, CMIP6 climate models, like their antecedents, produce LWCF errors enormously larger than the tiny annual increase in tropospheric forcing produced by GHG emissions.

Fourth, CMIP6 climate models, like their antecedents, produce uncertainties so large and so immediate that air temperatures cannot be reliably projected even one year out.

Fifth, CMIP6 climate models, like their antecedents, will have to show about 1000-fold improved resolution to reliably detect a CO2 signal.

Sixth, CMIP6 climate models, like their antecedents, produce physically meaningless air temperature projections.

Seventh, CMIP6 climate models, like their antecedents, have no predictive value.

As before, the unavoidable conclusion is that an anthropogenic air temperature signal cannot have been, nor presently can be, evidenced in climate observables.

I’ll finish with an observation made once previously: we now know for certain that all the frenzy about CO₂ and climate was for nothing. . . ."

Model Failure: ‘All Model Runs Warmed Faster Than Observations’ … CO2 Sensitivity Estimates ‘Unrealistically High’
By Kenneth Richard on 2. November 2020

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Scientists continue to document severe discrepancies between climate modeling and observations in newly published scientific papers.
1. Models run too hot and yield “unrealistically high” estimates of the climate’s sensitivity to CO2 forcing.
McKitrick and Christy, 2020
“All model runs warmed faster than observations in the lower troposphere and midtroposphere, in the tropics, and globally. On average, and in most individual cases, the trend difference is significant.”
“[W]e present evidence that the distribution of ECS [equilibrium climate sensitivity] values across the model is unrealistically high.”
2. Models can’t decide if cloud feedbacks are positive or negative. Half think they’re positive, the other half negative. . . .

Climate predictions based on modeling is for the birds.

PoS said:

Climate predictions based on modeling is for the birds.

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Absolutely.

Climate models produce the results based on what their designers believe.

New Climate Models (CMIP6) Offer No Improvement, Model Discrepancies As Large As The Last Version (CMIP5)
By Kenneth Richard on 24. December 2020

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The “unsatisfactorily large” magnitude of the discrepancies between models in estimating the various radiative contributions to Earth’s energy imbalance serves to undermine confidence that CO2’s small impact could even be detected amid all the uncertainty.
Scientists have engaged in offering their educated guesses, or estimates, of cloud radiative effects for decades.
In the latest models, CMIP6, the top of atmosphere (TOA) net cloud radiative effects (CRE) when considering clouds’ longwave and shortwave combined impact is somewhere between -17 W/m² and -31 W/m² (Wild, 2020). That’s a 24 W/m² spread in CRE modeling.
The discrepancy range between modeled estimates for downward longwave clear-sky radiation is 22.5 W/m². This is the component where CO2’s underwhelming 0.2 W/m² per decade impact (Feldman et al., 2015) is manifested. Modeling discrepancies are thus more than 100 times larger than CO2’s forcing contribution over a 10-year period.
Dr. Wild further reports that there is a generalized 10-20 W/m² magnitude modeling discrepancy in the “global energy balance components” estimates, such as latent heat flux (18 W/m²). These spreads in climate modeling are referred to as “unsatisfactorily large” and they may preclude accurate representation of the global energy cycle and water cycle.
“The substantial inter-model spread of 18 Wm−2 in the simulated global mean surface latent heat flux further points to considerable discrepancies not only in the representation of the global energy cycle, but also of the global water cycle in the CMIP6 models.”
So, unfortunately, no real model improvements to energy budget estimates have been realized over the course of decades. . . .