Guest essay by Mike Jonas
/Continued from Part 1
3. The Models can never work
In an earlier post, Inside the Climate Computer Models, I explained how the climate computer models, as currently structured, could never work.
Put simply, they are not climate models, they are weather models, because they operate on small(ish) pieces of ocean or atmosphere over very short periods (sometimes as little as 20 minutes). By definition, conditions in a small place over a short time are weather.
Figure 2.1. Ocean and atmosphere subdivision in the models. From the IPCC here.
But because their resolution has been weakened in order to be able to be run over long periods of time (a century or so), they are less accurate than the weather models used by the world’s weather bureaus. Those weather models can forecast conditions no more than a few days ahead. The climate models become inaccurate even sooner. All output from all climate computer models – as currently structured – is a work of fiction.
Confirmation of all of the above was recently provided by (US) National Center for Atmospheric Research (NCAR). They performed 40 climate model runs covering the 180-year period 1920 to 2100. All of the runs were absolutely identical except that “With each simulation, the scientists modified the model’s starting conditions ever so slightly by adjusting the global atmospheric temperature by less than one-trillionth of one degree.“.
The results from the 40 runs were staggeringly different. Predicted temperature changes for North America over the 1963-2012 period were shown, and they differ from each other by several degrees C over large areas. They even disagree on whether areas get warmer or cooler.
Think about it. By changing the model’s initial global temperature by a trillionth of a degree – ridiculously far below the accuracy to which the global temperature can be measured – and without any other changes, the model produced results for major regions that varied by several degrees C. The world’s weather stations will surely never be able to measure global temperature to anything like as small a margin as 0.000000000001 deg C, yet that one microscopic change alone causes a model’s results to change by several times as much as the whole of the 20th century global temperature change. And, of course, there are many other equally important parameters that cannot be established to anything like that kind of accuracy.
This NCAR report shows unequivocally that the climate models in their current form can never predict future climate.
4. The Tuning Disaster
4.1 How the Models were Tuned
In another earlier post, How reliable are the climate models?, I explained how the way in which the climate computer models were tuned led to major roles being assigned to the elements of climate that were least understood. Basically, when there was a discrepancy between observation and model results – and there were plenty of those – they fiddled the parameters till they got a match. [Yes, really!]. Anything that was well-understood couldn’t be fiddled with, so things they didn’t understand were used to fill the gaps.
The major problem that they had was that Earth’s climate had warmed much more over the ‘man-made CO2’ period (about 1950 onwards) than could be explained by CO2 alone, as shown in 2.5 (in Part 1). They manipulated the parameters for water vapour and clouds, without checking the physical realities, until they could match 20th-century temperatures. Both factors were portrayed as feedbacks to CO2. Bingo! The models showed all the late 20th-century temperature rise as being caused by CO2.
The modellers assumed that in the longer term cloud cover didn’t change naturally but changed only in reaction to – you guessed it – warming by CO2. So cloud cover as a natural process never participated in the model tuning, and all the warming of the ocean ended up being attributed to CO2. The only way that could happen was by the atmosphere warming the ocean.
No wonder that everything has gone pear-shaped since then.
4.2 Water Vapour Feedback
When the ocean warms – for any reason – there is more evaporation; about 7% more per degree C. Water vapour is a GHG, so that leads to more warming. That is all in the models, and it’s OK (apart from the reason for ocean warming).
But the models only allow for 2-3% more precipitation. In 2.1.1 (in Part 1) I cited evidence that precipitation also increased at the higher rate. The fifth IPCC report also virtually admitted a higher rate: “the global mean surface downward longwave flux is about 10 W m–2 larger than the average in climate models, probably due to insufficient model-simulated cloud cover or lower tropospheric moisture . This is consistent with a global-mean precipitation rate in the real world somewhat larger than current observational estimates.“. The model tuning process has therefore assigned more warming to water vapour feedback than it should have (the water cycle is part of the water vapour feedback).
Figure 1.2 (in Part 1) shows 78 Wm-2 of latent heat transfer from ocean to atmosphere. Much of that process occurs in the tropics, where the latent heat is transferred to the tops of clouds by tropical storms: the warm moist air is convected up until it is cooled enough for the water vapour to condense, releasing the latent heat and forming clouds, then it sinks until it is precipitated. So the latent heat is released in the cloud-tops. 4% (the difference between the full C-C 7% and the models’ 3%) of 78 Wm-2 is 3.1 Wm-2. When energy is released in the cloud-tops, nearly all of it will radiate upwards or be reflected upwards, so nearly all of it is lost to space.
From the water cycle alone, water vapour feedback is therefore overestimated in the models by something like 3 Wm-2. And it all comes from the way the models are tuned.
This is very significant: downward IR from a doubling of atmospheric CO2 is put at 3.7 Wm-2.
4.3 Cloud Feedback
The IPCC assign even more positive feedback to clouds than they do to water vapour. They even assign more warming to cloud feedback than they assign to CO2 itself. None of it comes from physics, it comes only from the model tuning process where they still needed a lot more warming from CO2 to match the observed global warming.
As illustrated in Figure 1.1 (in Part 1), and as described in Richard Lindzen’s “Iris” hypothesis, cloud feedback to global warming is likely to be negative. [Figure 1.1 is actually empirical confirmation of the “Iris” hypothesis].
It is difficult to overestate the stupidity of tuning climate models without checking the underlying physics, or at least acknowledging the huge uncertainties. To continue to treat models’ output as reliable predictions of future climate, despite multiple contrary evidence being presented, is surely hubris of the first order. It is certainly unscientific.
5. The Non-Linear Climate
At all times, it is necessary to bear in mind that Earth’s climate is a non-linear system.
This does make it rather difficult to unravel, because we are all much more used to linear thinking.
It means that any search for a correlation and any extrapolation of any data is even more dodgy than usual: a pattern which is clearly visible today might disappear in future. On finding the GCR-cloud connection, Laken et al (2010) comment: “However, [two other studies] may be inherently flawed, as they assume a first-order relationship (i.e. presuming that cloud changes consistently accompany GCR changes), when instead, a second-order relationship may be more likely (i.e. that cloud changes only occur with GCR changes if atmospheric conditions are suitable).“.
As the IPCC itself said (AR4 WG1): “we should recognise that we are dealing with a coupled nonlinear chaotic system, and therefore that the long-term prediction of future climate states is not possible.”. I suggest Kip Hansen’s article on WUWT for further reading.
/Continued in Part 3.
Mike Jonas (MA Maths Oxford UK) retired some years ago after nearly 40 years in I.T.
AMO – Atlantic Multidecadal Oscillation
APS – American Physical Society
AR4 – Fourth IPCC Report
AR5 – Fifth IPCC Report
C – Centigrade or Celsius
C-C – Clausius-Clapeyron relation
CAGW – Catastrophic Anthropogenic Global Warming
CO2 – Carbon Dioxide
ENSO – El Niño Southern Oscillation
EUV – Extreme Ultra-Violet
GCR Galactic Cosmic Ray
GHG – Greenhouse gas
IPCC – Intergovernmental Panel on Climate Change
IR – Infra-Red
ISCCP – International Satellite Cloud Climatology Project
ITO – Into The Ocean [Band of Wavelengths approx 200nm to 1000nm]
NCAR – (US) National Center for Atmospheric Research
nm – Nanometre
PDO – Pacific Decadal Oscillation
ppm – Parts Per Million
SCO – the Sun-Cloud-Ocean hypothesis
SW – Short Wave
THC – Thermohaline Circulation
TSI – Total Solar Irradiance
UAH – The University of Alabama in Huntsville
UV – Ultra-Violet
W/m2 or Wm-2 – Watts per Square Metre