We know the climate is changing because that’s what climate models tell us. But what exactly is a climate model, and are they cutting-edge science or modelling madness?
What is a climate model?
Climate models are used to describe the behaviour of all, or part, of the earth’s climate.
They can be simple computer programs that describe one or more components of the climate in a specific location (how temperature changes with cloud cover in Hobart, for example). Or they can be immensely complex systems that attempt to describe the behaviour of the earth’s entire climate system.
Models that simulate the climate of the entire earth are called general circulation models, or GCMs.
The two major components of the climate are the atmosphere and the ocean. Other elements include sea ice, land surface, snow cover, lakes and rivers.
The movements of the atmosphere and ocean currents are described using the Navier-Stokes equations.
These mathematical equations have been used for more than 200 years to describe the movement of fluids. As well as being used to model climate systems, these equations are used by engineers to describe water flow in pipes and air flow around objects such as aeroplane wings.
Can we predict the future?
The future state of the climate has been of interest to people for thousands of years. The Babylonians used cloud patterns to forecast the weather as far back as 650 BC, and Chinese and Indian astronomers independently developed weather prediction methods by 300 BC.
The practice of using a mathematical model to predict the weather has been around since the 1920s, but it wasn’t until the advent of computers in the 1950s that numerical weather predictions first produced realistic results. The first GCMs were developed in the late 1960s.


Weather vs climate
Numerical weather prediction (weather forecasting) and climate projections are closely related sciences.
Weather forecasting is about predicting the day-to-day variation in the atmosphere, including temperature, rainfall and humidity. Climate is the average weather pattern for a region.
In this way, climate projections seek to describe the condition of the atmosphere (and oceans) over long periods of time, often decades to centuries.
The weather varies on a daily basis, whereas the climate of a particular region is consistent and predictable.
For example, the climate of Hobart in winter sees rain falling on approximately half the days of each month with daily maximum temperatures around 10 – 15°C.
Climate projections (and climate models) do not tell us about the weather at any particular time, but about the average weather over a long period.
A climate model can’t tell us if Christmas Day, 2085, in St Helens (on Tasmania’s east coast) will be fine and sunny with a maximum temperature of 25°C.
But it can tell us the average (or “mean”, more accurately) temperature for December towards the end of the century, in this region, is likely to be 3°C warmer than it is now.
Climate models also tell us that on Tasmania’s east coast the end of the century will see more rain falling in summer, and that this rain is likely to fall in relatively more intense, short bursts.
Why should we trust climate models?
An important difference between numerical weather prediction and climate models is how they use observations.
In order to make a weather forecast as accurate as possible, observations are used to define the current state. As new observations become available these are incorporated into the simulation so that it becomes more accurate with time.
In contrast, climate models are initialised using an approximation of the current (or past) climate, but once running do not correct themselves using observations in any way.
That climate models produce a climate that is so similar to observations is purely due to the physical processes simulated in the model. This close comparison between observations and simulations is a strong argument for the efficacy of climate modelling.
A model state
In collaboration with the Tasmanian Government, the Antarctic Climate and Ecosystems Cooperative Research Centre recently completed its Climate Futures for Tasmania project.
The project featured a suite of climate models running from 1961 through to 2100 to generate highly detailed projections of the current and future climate of Tasmania.
The simulations were configured so that the concentration of CO₂ in the atmosphere matched the observed (increasing) CO₂ levels between 1961 and 2007 and then continued to increase in line with projected CO₂ emissions from 2007 to 2100.
Between 1961 and 1990 the simulations reported a state-wide mean temperature within 0.1°C of the observed mean Tasmanian temperature for the same period (see images above).
Similarly, the simulations reported a mean annual rainfall within five millimetres of the observed value of 1,390 milllimetres – an accuracy of around 99.6% (see images below).
The strong agreement between the simulations and observations shows just how capable climate models can be in simulating current climate.


While the ability to simulate current climate does not guarantee the ability to simulate future climate, it is an important precursor.
Today’s climate models can accurately reproduce current climate, and have been able to reproduce changes to the climate that have been observed in recent years.
Many of the processes that drive the climate are well understood and the ability to capture these processes in models has been tested and is constantly being improved.
Climate models are not perfect, but they are the best tool we have available for explaining the current behaviour of our climate and predicting likely changes to the planet’s future climate.
Stephen Prowse
logged in via LinkedIn
Surely we know that the climate is changing because of the data collected over the last several hundred years demonstrates a steady increase in global temperature. Models predicting the future are just that, models. They can help guide decision making but are not the truth. There is an obvious risk in believing that the models are real. Hence we know that climate is changing because that is what the data shows us not the models or has the distinction between data and models become so blurred there are no boundaries?
Derek Bolton
Retired s/w engineer
Observations can only tell you the climate has changed. They don't tell you why (coincident changes are just correlations), and don't tell you how it will change in the future.
The use of models is everywhere in science and engineering. Climate models are open to more uncertainty than many because of both the complexity of the models and the nonlinear relationships. The approach taken has been to run many reasonable models and take the distribution of results as an indication of probabilities; not ideal, but nobody has thought of anything better.
Paul Richards
Paul Richards is a Friend of The Conversation.
Stephen you have left out the importance of probability.
The higher the probability of an event, the more certain we are that the event will occur. Thus, probability in an applied sense is a measure of the likeliness that an event will occur.
Not forgetting human history shows there are 'Black Swan' events, occurring as outliers. Using your logic, we should dismiss probabilities and the possibility of 'Black Swan' events.
Would you be getting value for money if a science lecturer promoted that thought process?
Tim Paton
Automotive Engineer
The models came before the observations.
Going back decades, simple models have predicted long-term climate shifts due to increased CO2 concentration. As the models have developed, they've attempted to fill in the transition from current state to long term, and started to predict what the early changes will look like.
Only very recently have the observed climate trends become strong enough and sustained enough to start matching against models.
There seems to be a misconception among denialists…
Read moreStephen Prowse
logged in via LinkedIn
I agree with what you are saying re probability and "Black Swan" event so I am not sure what you are getting at. I would not be getting value for money if a science lecturer promoted the notion that a forecast from a model was a "truth". Setting aside the issue about what came first climate data or the model, generally models and data are developed/generated, often in parallel. This then leads to hypotheses which can then be tested. The contribution of greenhouse gas to climate change is a hypothesis which cannot be tested. This does not mean we should not be taking action in both adaption and mitigation but take into account the scientific process and the broader context.
Paul Richards
Paul Richards is a Friend of The Conversation.
"..... greenhouse gas to climate change is a hypothesis which cannot be tested"
That is entirely your opinion.
Peer review is a spam filter. Peer review is quality control. And peer review is independent of commercial interests or personal cognitive bias.
As "The Conversation" is an opinion based forum, you are entitled to yours, something we all can be grateful for. However, peer review gets us closer than opinion to the truth than any known method.
If modelling is considered in peer review, it also is closer to the truth than opinion.
Danderson
logged in via Twitter
And this comment is voted negative.
There are some clubs you are best denied entry to.
Ken Fabian
Mr
Those who are opposed to CO2 being a powerful greenhouse gas find it advantageous to undermine public trust in science, it's tools and it's methods. Since most people don't understand why or how well climate models work it's relatively easy to persuade them that they involve tweaking (cheating) to produce desired results - whilst passing over the fact that the results most desired by climate scientists are close mimicking of actual climatic processes in order to better understand them and to improve…
Read moreToby James
retired physicist
Ken, your argument lacks consistency. You write: ". . . smart and dedicated and honest people working full time trying to understand climate and predict the consequences of human impacts like emissions . . .
I for one consider that body of knowledge of the workings of our climate to be a jewel in the crown of human achievement;"
On one hand you plead the case for continued funding for those dedicated people who are trying to understand and predict climate. On the other hand, you regard climate…
Read moreMichael Ashley
Professor of Astrophysics at University of New South Wales
Stuart, thank you for this interesting article, and for pointing out the recent completion of the Climate Futures for Tasmania project. The images that you show comparing models with actual data for temperature and precipitation are very impressive.
James Szabadics
Technical Development and R&D Manager, Plantation Timber Industry
The images show the model mean is similar to the measured mean. One would hope that is the case. I would not get too excited however. If for example I made a climate model that wildly changed from run to run above and below the mean then my model mean and the measured mean would still be similar but each model run could be 6 sigma from the real world! I could paste pictures of mean comparison and you would be impressed!
James Szabadics
Technical Development and R&D Manager, Plantation Timber Industry
We know that the climate is changing because of measurements of various climate parameters not because of models.
Models produce theoretical data, not real world data. They are as good as the underlying assumptions and theory behind the model.
Measurements tell us we might be using a higher sensitivity to the certain parameter CO2 in climate models than we should. The models will be refined if the divergence becomes statistically significant I'm sure.
As a scientist its not a good idea to treat a model output as an observation, better to regard it as a predictive tool based on our current understanding. They are useful tools for prediction so long as we fully understand the nature of the system including all feedbacks and sensitivities in the right balance because only then will we get accurate and reliable prediction.
John Nicol
logged in via email @bigpond.com
Stuart Corney,
This comment may be too late to obtain any recognition.
However, I want to say that I am very heartend to see that a theoretical physicist has taken up the challenge of modelling the world's climate and in particular to note that the models (presumably your models) have demonstrated their ability to reproduce average temperatures for a distribution over Tasmania in the thirty years from 1961 to 1990.
As a physicist who spent much of my time dealing with theoretical models…
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