Beyond climategate: can we keep the politics and science of climate forecasting separate?


Any inaccuracies are likely to undermine many people’s faith in climate science

The pressure is on climate forecasters to give us more accurate predictions of impacts, such as rising sea levels, but ahead of the Durban climate summit scientists say we still have much to learn

When it comes to the environment, how does the world know how much long-term trouble it is in? Through climate modelling – using computer technology to predict climatic changes over years and decades in much the same way as tomorrow’s weather is forecast.

These projections are used for a multitude of purposes, from helping governments work out where to spend their money to drafting legislation and calculating insurance risks. More than that, they are the means by which we map the current and future effect of climate change on the planet – and thus on ourselves.

Its political and social importance has seen climate modelling come under more scrutiny than ever before. As a result the pressure is on climate scientists to be as precise as it is possible to be, not least because of the speed with which any perceived data discrepancies are seized upon by different interest groups.

In November 2009, emails were leaked from the Climatic Research Unit at the University of East Anglia that led to its being accused of manipulating data. While nothing in the emails cast doubt on the fact of manmade climate change, the researchers involved were perceived to have been secretive about the information they had gathered, and thus to have something to hide.

Last year the IPCC was forced to admit that a 2007 claim that Himalayan glaciers could melt away by 2035 was sourced not from peer-reviewed scientific literature but a 1999 media interview with a scientist.

Mistakes, cover-ups and inaccuracies have served to undermine many people’s faith in climate science at a time when its work is more important than ever.

Climate modelling has a different problem: based on forecast and projection, it is by definition an inexact science, but one upon which concrete decisions must be based if governments and societies are to assess risks and plan ahead. Somewhere in the grey area between prediction and policy, this uncertainty becomes more concrete.

Vicky Pope, head of climate science advice at the Met Office, says it is important to keep the politics and science separate. Problems arise, she says, when campaigning groups seek to use the science to make their own arguments. ‘The science needs to be seen to be objective, peer-reviewed and open, so people can ask questions.’

A step in this direction was made In July this year. After a two-year battle following ‘Climategate’, an Oxford academic won the right to get access to the UEA data, including 160 years’ worth of thermometer readings from 4,000 weather stations. As a result of the ruling, climate researchers from now on will be required to make their research publicly available.

How climate modelling works

Climate models translate the climate of the earth into mathematics. The simpler ones do so in order for scientists better to understand the processes and establish patterns. More complex examples (General Circulation Models) attempt to represent everything – clouds, air movement, rain, shrinking ice, ocean heat, as well as the interaction between all these things, which in effect define climate – as well as use archive information to model climates from the past, in order to make predictions for the future.

GCMs are currently the best means of creating a climate forecast, but because of the complexity of the weather systems they calculate can be difficult to analyse and understand. And even the very best models are only as good as our current level of knowledge and the computer technology available to us.

There are 22 major global climate models and many more regional ones that submit their data to the IPCC. It produces a report comparing and synthesising all of the data, noting discrepancies and scientific disagreements – some models have different ways of representing clouds, for example.

This makes it one of the most powerful ways to assess the uncertainty inherent in the process, says Vicky Pope. She adds that the IPCC process ‘isn’t perfect’ but significant improvements are made to it on an ongoing basis.

‘These are projections – there is no way to provide an accurate prediction of the future – and their goal and that of climate science is to produce a risk assessment of what the science is telling us about how the climate will change. It is up to politicians and society to decide on a response. Our role is simply to supply objective evidence and to represent the uncertainty inherent in the scientific process. It isn’t a question of right and wrong, but of trying to give a balanced assessment of what is certain and uncertain.’

How can we get more accurate forecasts?

But new research from the Lancaster Centre for Forecasting at Lancaster University Management School (LUMS) does suggest that current forecasts can be made more accurate.

Report co-author Robert Fildes, a forecast researcher, developing a simple statistical model that delivers better results when compared with previous climate forecasts, i.e. by adding certain data he has been able to match his figures more accurately with a historic forecast.

‘If the climate model was as good as it is possible to be, my simple statistical model should not have added any value. But broadly speaking, it does. My research revealed the limited availability of “proper” forecasts from these climate models, yet we are risking the world on them. The whole thing is based on a set of forecasts – for example, how high should our flood defences be? – and we need to know how accurate they are.’

Weather forecasters get immediate feedback, he points out: if they forecast rain for tomorrow, they know tomorrow whether they were right. While there are methodological problems inherent in forecasts with a 10-20-year horizon, Fildes is critical of the fact that climate scientists have ‘tended to say it’s too difficult and not perfect so we won’t do it. They can do it, and do it better.’

He stresses that his work should not be misinterpreted as being negative about climate modelling, but he says climate modelling would be improved if it absorbed the lessons learnt by the forecasting and economic communities over the past 20-30 years, primarily that rigorous evaluation and benchmarking methods – comparing forecasts against some established benchmark to assess their accuracy – are vital.

Another issue is that current GCMs are by no means exhaustive. In a recent report, Nasa climate change scientist James Hansen observed that current climate models do not factor in ‘climate forcing’ – changes that affect the energy balance of the planet – caused by aerosols, and as such deliver incorrect results. He calls it ‘the principal barrier to quantitative understanding of ongoing climate change. Until aerosol forcing is measured, its magnitude will continue to be crudely inferred, implicitly or explicitly, via observations of climate change and knowledge of climate sensitivity.’

Fildes argues that policymakers need to be responding to a wide range of other climate forcings – not simply greenhouse gases – and considering their effects regionally as well as globally. The IPCC climate modelling process is unreliable because it does not do so, he says, adding that the focus on greenhouse gases has been driven by a priori assumptions in the models themselves. This will have to change in the future, he adds.

‘Not to minimise the focus on greenhouse gases, but we need a more eclectic policy mix. Part of our research does suggest that the accumulative impact of greenhouse gas output on future world temperatures may be lower than the IPCC estimates – which is good news if true – but the point is to tackle the problem in a variety of different ways.’

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