T E N

T H I N G S

E VERYONE SHOULD KNOWA B O U T C L I M AT E M O D E L S
Andrew Montford
With a foreword by Steve Baker MP

The Global Warming Policy Foundation
Note 32

Ten Things Everyone Should Know About Climate Models
Andrew Montford
Note 32, The Global Warming Policy Foundation© Copyright 2022, The Global Warming Policy Foundation

Contents
About the author	iii

Foreword	v

Demand for climate policy rests largely on climate models	

1Climate models are very complex	

1

Climate models are not complex enough	2Climate models are tuned, climate models are fudged	

3

Climate models get the temperature of the Earth wrong	

3

Climate models get many other things wrong too	

4

Climate models do not conserve energy	

5Climate models can’t predict the future	

5

Everyone knows that climate models are failing, but no-one will say	

5

Climate models are causing harm	

6

About the author
Andrew Montford is deputy director of the Global Warming Policy Foundation.

iii

Foreword
By Steve Baker MP
Before I became an MP, I was a Royal Air Force engineer officer, working on
airworthiness. Following that, I completed an MSc in computer science andhad a career in software engineering. As a result, I have a keen interest in
software, critical systems and risk management: that is, how we decide what
to do when in jeopardy.
Airworthiness engineering is a serious business, and safety criticalsoftware engineering is exceptionally rigorous. However, there’s a growing
awareness that things are often rather different in academia. What goes onin university laboratories may often be of little interest outside the ivory tower, so it’s perhaps unsurprising that the approach of scientists to software
engineering is a little different to what happens in wider world. Why go to
the trouble of documenting your computer code if nobody is ever going tolook at it? Why use unit testing or a comprehensive version control system tomanage development? Coding standards? Pah! An experimental approach
is often taken, with code written and rewritten in a voyage of discovery, usually with little or no documentation, and no validation, rigorous or otherwise. Huge blocks of code may be found commented out without explanation. It is not software engineering; it is hacking, unfit for a good hobbyist.
Hacking may be fine if computer code genuinely has no impact beyond the laboratory, but with ministers increasingly looking to university researchers to provide support and justification for their policies – or even to
substantially direct policy – with the most profound impact on our liberty
and prosperity, such unprofessional standards can no longer be tolerated.
We have seen the implications during the Covid pandemic. The computer simulations offered by the Government’s scientific advisers have been
widely criticised, both for the poor quality of their code and the wild divergence of their predictions from subsequent reality. We are still assessing thefull collateral cost of inaccurate models deployed to inspire and justify lockdowns.
In the long-term, climate models may be even more impactful. The Net
Zero target in place, largely following from climate model predictions about
what the future may hold, has implications for every human being on theplanet alive today and for billions as yet unborn. If we get this wrong, humanity as a whole, now and in the future, will suffer the consequences. The
situation could scarcely be more serious.
Yet there is every sign that climate models are just as error strewn andjust as badly coded as many of the Covid models. There have long been signs
that climate model predictions are diverging from reality and things don’t
seem to be getting better. A recent article in Nature suggests that many ofthe most up-to-date models are entirely incompatible with observational records. But it also points out that they are being used to inform impact studies and environmental policy regardless.
Andrew Montford’s paper is therefore timely. It sets out, in an accessible way, some of the issues that afflict climate modelling, giving the public a sense of where we might be going wrong. It is hard to read it without
concluding we need to think again, with exceptional care, before accepting
computer simulations – of climate, or of anything else – as a guide to good
policy.
v

Demand for climate policy rests largely on
climate models
It is widely thought that the climate is changing in a significant
way. Views like this are commonly expressed as the idea that theweather has been unusual, often alongside some reference toextreme weather events. However, the climate changes all thetime and so it is difficult to say with any confidence that any recent trends are anything out of the ordinary.
Because of this, climatologists have to rely on computer
simulations in order to try to understand whether human carbon dioxide emissions are having a discernable effect and to
predict what might happen in the future.
These simulations, with names like global climate models
or Earth system models,1 hereafter ‘climate models‘, are therefore central to the policymaking process. All claims that the climate is doing something out of the ordinary and that it will dobad things in the future are only as reliable as the climate models behind them. It is therefore important that policymakers
understand climate models’ weaknesses, as well as their muchtrumpeted strengths.

Climate models are very complex
Since the first simulations of the climate were developed, climate models have become vastly more sophisticated, incorporating more and more aspects of the ocean–atmosphere system. They are now immensely complex, typically incorporatingof the order of a million lines of computer code. They bring inwhat scientists understand to be the key components of the climate system: the continents, the oceans, the atmosphere, heatfrom the sun and so on. The surface of the virtual Earth of thecomputer model is divided into a grid, above which a columnof the atmosphere rises and, for ocean squares, below whicha column of seawater descends. In each of these columns, thechanging climate has to be calculated using complex mathematics, with each of the components of the climate system andtheir interactions worked out.
This is no simple task, and it is a remarkable scientificachievement to have brought the computer models to thestage at which their output looks approximately like the real climate: in the virtual planets inside the climate models, cyclones
form in the tropics and move polewards just as in the real Earth.
However, although there are, say, monsoons in climate models,
they can still get nitty-gritty details – say the direction of thewinds that bring the short rains to East Africa (!)2 – back to front.
1 These are distinct concepts. Earth system models incorporate interactions
between the ocean-atmosphere system and the biosphere, whereas global
climate models do not.
2 Hirons L and Turner A (2018). ‘The impact of Indian Ocean mean-state
biases in climate models on the representation of the East African shortrains‘. Journal of Climate, 31(16): 6611--6631.

1

This superficial resemblance to the real Earth does not, however,demonstrate that the models are valid representations of the real climate. Indeed, it is generally accepted by those who study simulationthat a simple model is preferable to a complex one. As some haveobserved, the choice of the correct level of complexity depends on adetailed understanding of the system,3 which climate modellers do
not have.

Climate models are not complex enough
Despite their extraordinary complexity, climate models may not becomplex enough. The grid squares on a climate model might be 50
km across. It is possible to calculate some aspects of the climate system at this scale from basic physical principles and, indeed, prominent scientists are keen to state that climate models are derived frombasic physics. However, for some components of the climate system
the physics involved takes place at scales that are too small to deal
with in this way. For example, the greenhouse effect takes place atatomic scales; clouds, plants and many features of the landscape are
more likely to be measured in metres or centimetres rather than kilometres. The Met Office’s HadCM3 model’s grid size essentially misses
most of the Andes because that mountain range is too narrow to be
represented,4 raising obvious question marks over the validity of its
predictions for South America’s climate. For many parts of the climate
system it is therefore only possible to work with an average figure
for the grid square. This introduces major uncertainties, since it is
impossible to know whether these so-called “parameterisations” remain valid as the climate changes. One recent paper found that if you
altered the order in which the parameterisations were calculated ina climate model, you ended up with profoundly different answers.5
Which order is right is anyone’s guess.
In addition, there are numerous factors that scientists have identified as potential influences upon the climate but where the magnitude of the effect, if any, has yet to be determined. There are many examples, but to consider just one, a 2013 NASA workshop heard how
some types of ultraviolet radiation from the sun could vary in intensity by an order of magnitude over time. Ultraviolet affects the chemistry and hence the physics of the atmosphere,6 but what, if anything,
this variation means for the climate is unclear and we are similarly
ignorant of what any of these ‘known unknowns‘ might mean. The
3 Chwif L and Paul RJ. On simulation model complexity. http://www.simul8.com/
support/newsletter/chwif.pdf.
4 Smith L, The user made me do it: Seamless forecasts, higher hemlines andcredible computation. Available at: http://www.eas.gatech.edu/sites/default/files/
SmithTalkGT.pdf. Smith notes that the Andes land surface in the simulation is as
much as two kilometres lower than in the real mountains.
5 Donahue A and Caldwell M (2018) Impact of physics parameterization orderingin a global atmosphere model. Journal of Advances in Modeling Earth Systems; 10:481–499.
6 The discussion referred to ‘extreme ultraviolet radiation‘, rather than UV per se.
See ‘Solar variability and terrestrial climate‘. NASA Science News, 8 January 2013.
http://science.nasa.gov/science-news/science-at-nasa/2013/08jan_sunclimate/.

2

point that there are almost certainly also ‘unknown unknowns‘ needhardly be made.
These gaps and simplifications in the climate model mean that it
is not possible to accept them as valid representations of the climate
system that can provide robust predictions about how the Earth will
respond to anthropogenic carbon emissions.

Climate models are tuned, climate models are
fudgedPerhaps it is because of the complexity, or perhaps because of ourrelative lack of knowledge of the climate system, but when climate
models are put together they rarely perform in a realistic manner,with too much or too little heat being retained by the virtual atmosphere. This results in the virtual temperatures rapidly drifting awayfrom the observed ones.
In order to address this problem, climate models are ‘tuned‘ – inother words some of the parameterisations are arbitrarily adjusted– so as to make the model warm at a more realistic rate.7,8 An example would be to adjust the way clouds are represented in the model.
Clouds are one of the great areas of uncertainty in the climate system, so many of the possible parameterisations are plausible. Different combinations of input values could give a virtual global temperature record similar to the real one, but completely different estimatesof future warming.9 Which climate future is the real one is, again, anyone’s guess, and is therefore possible to simply pick the one thatgives a desired answer.
The ability to tune the output in this way gives climatologists a‘fudge factor‘ and makes claims that climate models are ‘based on
fundamental physics‘ rather hollow. At best they are ‘largely‘ basedon physics.
Another fudge factor is the cooling effect of atmospheric pollution – so-called ‘aerosols’. It has been noted that climate models thathave high temperature sensitivity to carbon dioxide also have highestimates of the cooling effects of aerosols. Estimates for low-sensitivity models are lower. This means that both high- and low-sensitivity models can reproduce recent temperature history, but it is clearthat the pollution estimates are being determined by the required
answer rather than being derived from observations or from physics.

Climate models get the temperature of the Earthwrong
Although climatologists are able to fudge their way to the correct
rate of warming, they get the temperature of the Earth wrong. This is
7 Mauritsen T, et al. “Tuning the climate of a global model.” Journal of Advances inModeling Earth Systems 4.3 (2012).
8 Voosen P (2016) “Climate scientists open up their black boxes to scrutiny”.
Science; 354(6311): 401--402.
9 Zhao M, et al. (2106) “Uncertainty in model climate sensitivity traced torepresentations of cumulus precipitation microphysics”. Journal of Climate, 29,
543–560.

3

perhaps unsurprising, because, among other things, they get
the amount of solar energy hitting the Earth’s surface badly
wrong.10
The global surface temperature records and the climate
model outputs are expressed as anomalies from a long-term
average rather than as the simple temperature. However, thismanner of presentation hides the important fact that the virtual temperature in climate model outputs varies considerably– by as much as 3°C.11 Clearly they cannot all be right, and theimpact for those that are wrong is significant because many
important climate processes are temperature dependent. One
example is the amount of heat reflected from the polar ice
caps, which depends on the area of ice, something that willvary significantly if temperature changes by 3°C.
It is often argued that despite such difficulties, climate
models have still been able to reproduce the twentieth century temperature history. However, many of the latest generation of models produce climate histories that are entirely incompatible with the observations.12 Moreover, as noted above,
because of the availability of various ‘fudge factors‘ even when
they appear to perform well, it may be misleading. The true
test of a computer model’s reliability is its predictive ability
and here the climate models do badly, with the observed surface temperatures frequently on the cusp of falling outside therange of temperature predictions deemed ‘likely‘ by the IPCC,
and predictions for higher levels of the atmosphere performing even worse.

Climate models get many other things wrong
too
Climate models haven’t just got the surface temperatures
wrong. There are many other features of the climate systemthat are incorrectly simulated. Some climatologists have suggested that the failure to get the surface temperatures correctis because the real-world climate is transporting heat to thedeep oceans, an explanation which, even if correct, suggeststhat a major climate subsystem is not being correctly captured.
Climate models predict that the stratosphere should coolas the surface warms, but in fact the only cooling observed has
been in the wake of volcanic eruptions. Between times, thestratosphere has been warming. The situation is just as bad in
the troposphere, where climate models predict a rapid warming that has in practice not materialised.
10 The average error is about 7 Wm-2, while the energy imbalance thatis said to be causing global warming is only 0.5–1 Wm-2. See Wild M, et al.
(2015) ‘The energy balance over land and oceans: an assessment basedon direct observations and CMIP5 climate models‘. Climate Dynamics, 44:
3393–3429, who say that this is a ‘long-standing issue in climate modeling‘.
11 See IPPC WGI, AR5 Chapter 9, Figure 9.8.
12 Z Hausfather et al. (2022) ‘Climate simulations: recognize the “hotmodel“ problem’. Nature, 4 May.

4

If climate models are bad at temperature predictions, they
are even worse at rainfall. Even the IPCC describes their ability
as only ‘modest‘, while others have said they are ‘useless‘, noting that their predictive abilities are even worse than a naive
forecast based on extrapolating the long-term mean.
Because climate models tend to be tuned to a global average of some kind, they appear able to ‘hindcast‘ recent temperature history. However, when their output is compared ata more detailed level it becomes clear that the use of an average is disguising many biases and errors. No climate model
has proven able to provide skillful predictions of climate at aregional level.

Climate models do not conserve energy
It is a fundamental physical principle that energy should beconserved – in other words that the total energy of a systemshould remain constant. So once a climate model has reachedequilibrium, the heat arriving from the virtual sun into the virtual atmosphere should equal the heat leaving. However, it has
been observed that the most up-to-date climate models tend
to reach equilibrium when the two figures are not equal. In
most of these virtual worlds the heat arriving at equilibrium isgreater than the heat leaving, indicating that some of this heatis being ‘lost‘ and thus a fundamental physical principle is being breached.13 This raises enormous doubts over the validityof climate model predictions.

Climate models can’t predict the future
The problems noted in earlier parts of this paper should convince the reader that, for the moment at least, computer models are incapable of saying much about the future climate. The
IPCC acknowledges this in part, noting that predicting the future state of the climate at any point in the future is not possible even in theory because the climate is a chaotic system.
They suggest that the focus should instead be on predicting
a range of possible future climates and assigning probabilities
to each. However, the experience of the last two decades suggests that even the wide range of possibilities predicted by theclimate models is not wide enough and tends to incorporate
far too much global warming and not nearly enough naturalvariability.

Everyone knows that climate models are failing, but no-one will say
The inadequacies of climate models are well-recognised within the field. But as Leonard Smith, a prominent (mainstream)
researcher noted, their output is nevertheless being sold to
13 Mauritzen T et al. (2012) ‘Tuning the climate of a global model‘. Journalof Advances in Modeling Earth Systems; 4: M00A01.

5policymakers as if it were adequate to support decision-making,with the contents expressed in terms that allow for ‘plausible
deniability‘.14 Moreover, according to Smith, a joint statementof support for climate models by a group of learned societies
was cancelled once those involved in its drafting learned of how
systematic errors were being hidden by the presentation of theresults as anomalies.15
In the famous words of the statistician George Box, all models are wrong, but some are useful. This saying, frequently repeated by climatologists, is undoubtedly true, but does rather
beg the question of what precisely they are useful for. Climatologists have no choice except to use computer models to try
to understand the way the Earth works, so for this purpose they
are not only useful, but in fact essential. Their relevance to thepolicy arena however is far from clear. Policymakers are often
misled into thinking that climate models are providing robust
predictions about the future. The less reputable scientists, often
in positions of responsibility, point to the match between twentieth century temperatures and the climate models to emphasise their case.
It is only once the degree of tuning and fudging and thesimultaneous failure to match other climate indicators and thewholesale failure out of sample is seen that it is possible to understand just how misleading this is.

Climate models are causing harm
Despite the many failings of climate models, their predictions
continue to be accepted as valid by politicians. Policymakers areusing climate model output as a justification for decisions thathave immediate and appalling consequences for the developing world and can only be justified as necessary evils; unfortunate steps that are necessary as a way to avoid the still greater
evil of climate change. One example is the decision in 2007 todivert a significant proportion of the world’s crops to biofuels,
which caused widespread hunger in poor countries and was, inthe words of one UN official ‘a crime against humanity‘. Anotheris the decisions by the UK and US to end overseas aid for projects to develop fossil-fuelled power stations, depriving some of
the world’s poorest of access to energy and condemning themto cook on open hearths, something that is known to cause
widespread premature death.

14 Smith L, ‘The user made me do it: Seamless forecasts, higher hemlines
and credible computation‘. Available at: http://www.eas.gatech.edu/sites/
default/files/SmithTalkGT.pdf. ‘Information we are supplying which is not‘adequate for purpose‘ is being interpreted as if it was. “Plausibile Deniability”[sic] seems a poor aim to me.’15 The source is Smith’s talk again. ‘At least one united (multi-society)statement of support are failed to appear due to learning the systematic
errors “masked” when anomalies are plotted.‘

6

7

8

About the Global Warming Policy Foundation
People are naturally concerned about the environment, and want to see policies that protect it,
while enhancing human wellbeing; policies that don’t hurt, but help.
The Global Warming Policy Foundation (GWPF) is committed to the search for practical policies. Our aim is to raise standards in learning and understanding through rigorous research andanalysis, to help inform a balanced debate amongst the interested public and decision-makers.
We aim to create an educational platform on which common ground can be established, helping
to overcome polarisation and partisanship. We aim to promote a culture of debate, respect, and ahunger for knowledge.
Views expressed in the publications of the Global Warming Policy Foundation are those of theauthors, not those of the GWPF, its trustees, its Academic Advisory Council members or its directors.

THE GLOBAL WARMING POLICY FOUNDATION
Director
Honorary President
Benny Peiser
Lord Lawson
BOARD OF TRUSTEESDr Jerome Booth (Chairman)
Steve Baker MP
Professor Peter Edwards
Kathy Gyngell

Professor Michael Kelly
Terence Mordaunt
Graham Stringer MP
Professor Fritz Vahrenholt

ACADEMIC ADVISORY COUNCIL

Professor Christopher Essex (Chairman)
Professor J. Ray Bates
Sir Ian Byatt

Professor Richard Lindzen
Professor Ross McKitrick
Professor Robert Mendelsohn

Dr John Constable
Professor Vincent Courtillot
Professor Peter Dobson
Christian Gerondeau
Professor Larry Gould
Professor William Happer
Professor Ole Humlum
Professor Gautam Kalghatgi
Professor Terence Kealey
Bill Kininmonth
Brian Leyland

Professor Garth Paltridge
Professor Ian Plimer
Professor Gwythian Prins
Professor Paul Reiter
Professor Peter Ridd
Dr Matt Ridley
Sir Alan Rudge
Professor Nir Shaviv
Professor Henrik Svensmark
Dr David Whitehouse

GWPF NOTES
1Matt Ridley

A Lukewarmer's Ten Tests

2
3
4
5
6
7
8
9
10
11

Susan Crockford
Ross McKitrick
Andrew Montford
Andrew Montford
VariousMichael Kelly
David Whitehouse
Anthony Kelly
Susan Crockford
Andrew Montford

Ten Good Reasons Not to Worry About Polar Bears
An Evidence-based Approach to Pricing CO2 Emissions
Climate – Public Understanding and Policy Implications
Consensus? What Consensus?
The Geological Perspective of Global Warming: A Debate
Technology Introductions in the Context of Decarbonisation
Warming Interruptus: Causes for the Pause
Global Warming and the Poor
Healthy Polar Bears, Less Than Healthy Science
Fraud, Bias and Public Relations

12
13
14
15

Harry Wilkinson
Peter Lilley
Constable and Hughes
Patricia Adams

UK Shale Developments
The Helm Review and the Climate-Industrial Complex
Bubble or Babble?
The Road from Paris: China's Climate U-Turn

16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32

Mikko PaunioJohn Christy
Gordon Hughes
J. Ray Bates
Andrew Montford
Andrew Montford
David Whitehouse
Gautam Kalghatgi
John Constable
William M Briggs
Ross McKitrick
William M Briggs
J. Ray Bates
Susan Crockford
Michael Kelly
Michael Kelly
Andrew Montford

Save the Oceans: Stop Recycling Plastic
The Tropical Skies: Falsifying Climate Alarm
Who's the Patsy? Offshore Wind's High-stakes Poker Game
What Caused Hurricane Lorenzo?
Reducing Emissions Without Breaking the Bank
£3 Trillion and Counting: Net Zero and the National Ruin
The Next Solar Cycle and Why it Matters for Climate
The Battery Car Delusion
Who Are They Fooling? The UK Energy White Paper 2020
The Climate Blame Game: Are We Really Causing Extreme Weather?
Suboptimal Fingerprinting
How the IPCC Sees What Isn’t There
Polar Sea Ice and the Climate Catastrophe Narrative
The State of the Polar Bear 2021
Achieving Net Zero: A Report from a Putative Delivery Agency
Net Zero for New Zealand
Ten Things Everyone Should Know About Climate Models

For further information about the Global Warming Policy
Foundation, please visit our website at www.thegwpf.org.
The GWPF is a registered charity, number 1131448.