Modelling the future.

Can we trust the predictive output of computer modelling?

I would be the first to admit that this is not an area where I have anything more than general knowledge.  However, what prompted me to think about this topic was a chance conversation with someone here in Payson.  We were chatting over the phone and this person admitted to being less than fully convinced of the ’cause and effect’ of man’s influence on the global biosphere.

When I queried that, what was raised was the idea that all modelling algorithms used in climate change predictions must incorporate mathematical constants.  I continued to listen as it was explained that, by definition, all constants were, to some degree, approximations.  Take, for example, the obvious one of the constant π, that Wikipedia describes as: a mathematical constant that is the ratio of a circle’s circumference to its diameter. Pi, of course, would have to be rounded if it was to be used in any equation.  Even taking it to thirty decimal places, as in 3.14159 26535 89793 23846 26433 83279, would mean rounding it to 3.14159 26535 89793 23846 26433 83280 (50288 being the 30th to 35th decimal places).

OK, so I must admit that I was leaning to the viewpoint that this person had a valid perspective.  I then asked Martin Lack, he of Lack of Environment and a scientifically trained person, for his thoughts.  The rest of this post is based on the information that Martin promptly sent me.

One of the links that Martin sent was to this post on the Skeptical Science blogsite.  That post sets out the common skeptics view, namely:

Models are unreliable
“[Models] are full of fudge factors that are fitted to the existing climate, so the models more or less agree with the observed data. But there is no reason to believe that the same fudge factors would give the right behaviour in a world with different chemistry, for example in a world with increased CO2 in the atmosphere.”  (Freeman Dyson)

The author of the Skeptical Science posting responds,

Climate models are mathematical representations of the interactions between the atmosphere, oceans, land surface, ice – and the sun. This is clearly a very complex task, so models are built to estimate trends rather than events. For example, a climate model can tell you it will be cold in winter, but it can’t tell you what the temperature will be on a specific day – that’s weather forecasting. Climate trends are weather, averaged out over time – usually 30 years. Trends are important because they eliminate – or “smooth out” – single events that may be extreme, but quite rare.

Climate models have to be tested to find out if they work. We can’t wait for 30 years to see if a model is any good or not; models are tested against the past, against what we know happened. If a model can correctly predict trends from a starting point somewhere in the past, we could expect it to predict with reasonable certainty what might happen in the future.

So all models are first tested in a process called Hindcasting. The models used to predict future global warming can accurately map past climate changes. If they get the past right, there is no reason to think their predictions would be wrong. Testing models against the existing instrumental record suggested CO2 must cause global warming, because the models could not simulate what had already happened unless the extra CO2 was added to the model. All other known forcings are adequate in explaining temperature variations prior to the rise in temperature over the last thirty years, while none of them are capable of explaining the rise in the past thirty years.  CO2 does explain that rise, and explains it completely without any need for additional, as yet unknown forcings.

I strongly recommend you read the full article here.  But I will republish this graph that, for me at least, is a ‘slam dunk’ in favour for modelling accuracy.

Sea level change. Tide gauge data are indicated in red and satellite data in blue. The grey band shows the projections of the IPCC Third Assessment report (Copenhagen Diagnosis 2009).

Not only does this show that the data is within the range of projections of the modelled output, more seriously the data is right at the top end of the model’s predictions.  The article closes with this statement:

Climate models have already predicted many of the phenomena for which we now have empirical evidence. Climate models form a reliable guide to potential climate change.

There is a more detailed version of the above article available here.  Do read that if you want to dig further down into this important topic.  All I will do is to republish this,

There are two major questions in climate modeling – can they accurately reproduce the past (hindcasting) and can they successfully predict the future? To answer the first question, here is a summary of the IPCC model results of surface temperature from the 1800’s – both with and without man-made forcings. All the models are unable to predict recent warming without taking rising CO2 levels into account. Noone has created a general circulation model that can explain climate’s behaviour over the past century without CO2 warming. [my emphasis, Ed.]

Finally, back to Lack of Environment.  On the 6th February, 2012, Martin wrote an essay Climate science in a nut fragment.  Here’s how that essay closed:

Footnote:
If I were to attempt to go even further and summarise, in one single paragraph, why everyone on Earth should be concerned about ongoing anthropogenic climate disruption, it would read something like this:

Concern over anthropogenic climate disruption (ACD) is not based on computer modelling; it is based on the study of palaeoclimatology. Computer modelling is based on physics we have understood for over 100 years and is used to predict what will happen to the atmosphere for a range of projections for CO2 reductions. As such, the range of predictions is due to uncertainty in those projections; and not uncertainties in climate science. Furthermore, when one goes back 20 years and chooses to look at the projection scenario that most-closely reflects what has since happened to emissions, one finds that the modelled prediction matches reality very closely indeed.

In his email, Martin included these bullet points.

  • Concern over anthropogenic climate disruption (ACD) is not based on computer modelling.
  • It is based on our understanding of atmospheric physics (and how the Earth regulates its temperature).
  • Computer modelling is based on this physics (which we have understood for over 100 years).
  • Models have been used to predict temperature and sea level rise for a range of projections for CO2 emissions. 
  • The wide range of predictions was due to uncertainty in those emissions projections not uncertainties in climate science. 
  • This can be demonstrated by looking at predictions made over 20 years ago in light of what actually happened to emissions.
  • The model predictions for both temperature and sea level rise are very accurate (if not slightly under-estimating what has happened).

Sort of makes the point in spades!  The sooner all human beings understand the truth of what’s happening to our planet, the sooner we can amend our behaviours.  I’m going to pick up the theme of behaviours in tomorrow’s post on Learning from Dogs.

Finally, take a look at this graph and reflect!  This will be the topic that I write about on Thursday.

9 thoughts on “Modelling the future.

  1. I agree with you that the SkS graph of sea level rise is one of the best demonstrations of the accuracy of previous model predictions (the equivalent graph of temperature rise is just as good).

    Although I am, as ever, grateful for your support for my humble efforts to improve the global distribution of environmental sanity, I am not sure it was really necessary to reproduce my final paragraph and the list of bullet points (which was meant to be a more digestible version of the same text)…?

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  2. Well, okay then! Totally out of my element but have to say that in reading this twice over I was able to understand for the most part the information being relayed. I am far from a genius or university graduate but I do follow the environmental belief that we must do something to stop the damage we are causing to our planet. Martin, I require every bit of information and explained to me in more than one way, I agree with Paul on this one, adding the bullet points further helped me understand the piece. Therefore, thank you both for the continued enlightenment.

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    1. Merci, what a wonderful comment! Such feedback really does make a difference to the sad individuals such as me and the others who write blogs that are featured on Learning from Dogs! Best wishes, Paul

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  3. Computer models are as good as one thinks matters. All the models have been undershooting the ice disappearance. Obviously because some non linear positive feedbacks have not been considered. The simplest one is that the cold layer of water in the Arctic is only 200 to 300 meters thick. It will warm up, and then the ice will be gone. It could be in 5 years max.

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    1. I have been previously dismissed as an “alarmist” for suggesting the ice could all be gone in 5 years. I think the data suggest that would be at the very extreme end of what my possibly happen. 10 to 15 years looks most likely.

      On the subject of who exactly is being “alarmist” this new video from uknowispeaksense is very instructive:

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    2. That’s a very good point – the models are as good as one think matters! I suspect that we haven’t seen the last on ‘non-linear feedbacks’ in terms of ocean currents, global weather, Gulf Stream, and more! The power of that Chinese curse, “May you live in interesting times!” is not to be under-estimated! As always, Patrice, thank you for your input. Paul

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  4. Must just insert a personal comment just here.

    I am incredibly grateful for all the people who chose to ‘follow’ Learning from Dogs, now hovering just under 490. But I am also hugely appreciative of the ‘Likes’ that are attached to my posts.

    So many others out there have such interesting websites and blogs that I sense that the community that we all represent in so many different ways may be the salvation of us all.

    Nonetheless, I can’t resist highlighting just one person who recently attached a ‘Like‘ to this post: Patrick Latter.

    Just go across to his website and admire the breath-taking photography!

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