Date: 07/06/21
Dr David Whitehouse, GWPF Science Editor
The researchers found that when compared to observations, almost every CMIP5 model fails, no matter whether the multidecadal variability is assumed to be forced or internal.
The basic questions for climate models is whether they realistically simulate observations, and to what extent can future climate change be predicted? It’s an important concept as political and environmental action is predicated upon it.
A new paper by Timothy DelSole of George Mason University and Michael Tippett of Columbia University looks into this by attempting to quantify the consistency between climate models and observations using a novel statistical approach. It involves using a multivariate statistical framework whose usefulness has been demonstrated in other fields such as economics and statistics. Technically, they are asking if two time series such as observations and climate model output come from the same statistical source.
To do this they looked at the surface temperature of the North Atlantic which is variable over decadal timescales. The reason for this variability is disputed, it could be related to human-induced climate change or natural variability. If it is internal variability but falsely accredited to human influences then it could lead over estimates of climate sensitivity. There is also the view that the variability is due to anthropogenic aerosols with internal variability playing a weak role but it has been found that models that use external forcing produce inconsistencies in such things as the pattern of temperature and ocean salinity. These things considered it’s important to investigate if climate models are doing well in accounting for variability in the region as the North Atlantic is often used as a test of a climate model’s capability.
The researchers found that when compared to observations, almost every CMIP5 model fails, no matter whether the multidecadal variability is assumed to be forced or internal. They also found institutional bias in that output from the same model, or from models from the same institution, tended to be clustered together, and in many cases differ significantly from other clusters produced by other institutions. Overall only a few climate models out of three dozen considered were found to be consistent with the observations.
Read the full article at The GWPF
NOTE: The link above to the paper seems to have been removed. Here is a local copy:
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John Stossel is fighting the good fight, but he’s being censored…..errr, fact checked and it’s a joke.
” Overall only a few climate models out of three dozen considered were found to be consistent with the observations.”
Blind luck
Models are Models
I remember when computers were first used
there was a anachronym GIGO (garbage in garbage out)
unfortunately these humanities (climate change scientists – who are paid prostitutes of the anti society rich and famous) dont know how to do proper experimental design
anyone who builds simulation models for production plants knows you start simple and test against physical experimental results
Obviously they dont care as they get the money without having to prove that there study was worth our tax payer money
Maybe we need a KPI for institutions and if they fail no more gold
The GCMs are fundamentally wrong. One mistake is revealed by Dr. Christy’s graph showing GCM calculated temperature increase rates averaging about twice measured. Another mistake is the way they handle water vapor (WV). It is calculated within the GCMs with the result being that calculated relative humidity (RH) is approximately constant as the temperature increases (some models simply assume constant RH as the temperature increases). The GCMs should use measured WV.
WV has been accurately measured globally using satellite instrumentation and reported as Total Precipitable Water (TPW) since Jan 1988. The measured WV increase has been about 1.49% per decade. The measured WV trend has been about 43% more than possible and is more than the trend calculated by the GCMs. This is shown graphically at https://drive.google.com/file/d/1Ntl57AEsPYfTppC0DUziRBnq0NV42g5B/view?usp=sharing which also has links to supporting data and analyses.
Since both have been accurately measured worldwide, more than 7 WV molecules have been added for each added CO2 molecule.
WV is a greenhouse gas (ghg). The part of the WV increase that is not accounted for in the GCMs is approximately the amount above that which would result from just temperature increase. This ‘extra’ WV is enough to account for all of the average global temperature increase attributed to humanity. The ‘extra’ WV comes mostly (about 90%) from increasing irrigation.
Another mistake in the GCMs is failure to account for the delay between the time a ghg molecule absorbs a photon and when it emits one. This delay is called relaxation time. It allows radiation energy absorbed by CO2 molecules in the troposphere (the troposphere is below about 8-16 km, depending mostly on latitude; higher at equator) to be ‘redirected’ to WV molecules which emit it at a longer wavelength. Much of the outward directed radiation from WV molecules in the troposphere makes it all the way to space. Details are in the links at the above graph.
Regardless of the WV increase that might result from any and all feedbacks, human activity has added more. Failure to account for actual measured WV is a mistake. Thermalization and the huge gradient in WV of about 1200 to 1 on average, ground level to tropopause, are what allow much of the energy absorbed by CO2 in the troposphere to be redirected to WV molecules and radiated directly to space.
If you’re in front behind do some adjustment and you can be behind in front
A clever programmer could make a ‘ model ‘ work backwards ,
but highly unlikely to work forwards ,
No science involved , just computer language .
The paper seems to have been removed from the GMU FTP website. I suspect Mann had something to do with the removal, by reporting it to the journal. I have restored a local copy and added a link to it at the end of the article.
I downloaded a copy from GMU just now, using download.file in R.