Complexity, credibility and confidence: how to communicate climate model forecasts?August 17, 2007 at 2:08 pm | Posted in BAS, British Antarctic Survey, Cambridge, Climate models, Lenton, Oxford, Royal Society, Schellnhuber, Smith, Stainforth, Tyndall Centre, University of East Anglia | 1 Comment
- 16 August 2007
- Fred Pearce
- Magazine issue 2617
SOME climate tipping points may already have been passed, and others may be closer than we thought, it emerged this week. Runaway loss of Arctic sea ice may now be inevitable. Even more worrying, and very likely, is the collapse of the giant Greenland ice sheet. So said Tim Lenton of the University of East Anglia, UK, speaking on Monday at a meeting on complexity in nature, organised by the British Antarctic Survey in Cambridge.
Lenton warned the meeting that global warming might trigger tipping points that could cause runaway warming or catastrophic sea-level rise. The risks are far greater than suggested in the current IPCC report, he says.
Yet climate modellers are in a quandary. As models get better and forecasts more alarming, their confidence in the detail of their predictions is evaporating.
The IPCC says the Greenland ice sheet will take at least 1000 years to melt. But Lenton’s group – whose members include John Schellnhuber, the chief scientist on climate change at the recent G8 meeting in Germany – says the sheet could break up within 300 years, raising sea levels by 7 metres. This would flood hundreds of millions of people or more out of their homes. “We are close to being committed to a collapse of the Greenland ice sheet,” Lenton says. “But we don’t think we have passed the tipping point yet.” The calculations show the Greenland collapse could be triggered by temperatures 1 °C warmer than today’s, of which 0.7 °C is already “in the pipeline”, held up by time lags in the system.
“We are close to being committed to a collapse of the Greenland ice sheet, but we don’t think we have passed the tipping point”
Lenton’s study has identified eight dangerous tipping points that could be passed this century. Several could have a cascade effect, with each triggering the next, he says.
The tipping points include a collapse of a global ocean circulation system known as the thermohaline circulation. Besides shutting down the Gulf Stream, this could also “switch off” the Asian monsoon and warm the Southern Ocean, perhaps destabilising the West Antarctica ice sheet. This would cause a further 7-metre rise in sea levels. Likewise, warming may cause a near-permanent El Niño in the Pacific, which would hasten a runaway burning of the Amazon rainforest and its disappearance by mid-century.
The existence of potential climate-change tipping points should dramatically alter economists’ assessments of how much climate change we should prevent, said Lenton. The trouble is, the discovery of tipping points has also unmasked growing uncertainty about the reliability of conventional climate models.
At the Cambridge meeting Lenny Smith, a statistician at the London School of Economics, warned about the “naïve realism” of current climate modelling. “Our models are being over-interpreted and misinterpreted,” he said. “They are getting better; I don’t want to trash them per se. But as we change our predictions, how do we maintain the credibility of the science?” Over-interpretation of models is already leading to poor financial decision-making, Smith says. “We need to drop the pretence that they are nearly perfect.”
He singled out for criticism the British government’s UK Climate Impacts Programme and Met Office. He accused both of making detailed climate projections for regions of the UK when global climate models disagree strongly about how climate change will affect the British Isles.
Smith is co-author, with Dave Stainforth of the Tyndall Centre for Climate Change Research in Oxford, of a paper published this week on confidence and uncertainty in climate predictions (Philosophical Transactions of the Royal Society A, DOI: 10.1098/rsta.2007.2074*). It is one of several papers on the shortfalls of current climate models.
Some authors say modellers should drop single predictions and instead offer probabilities of different climate futures. But Smith and Stainforth say this approach could be “misleading to the users of climate science in wider society”. Borrowing a phrase from former US defence secretary Donald Rumsfeld, Smith told his Cambridge audience that there were “too many unknown unknowns” for such probabilities to be useful.
Policy-makers, he said, “think we know much more than we actually know. We need to be more open about our uncertainties.” Meanwhile, the tipping points loom.
From issue 2617 of New Scientist magazine, 16 August 2007, page 13
* Confidence, uncertainty and decision-support relevance in climate predictions authors and abstract follow:
D.A. Stainforth1, 3, M.R. Allen2, E.R. Tredger3, L.A. Smith3
1Tyndall Centre for Climate Change Research, Environmental Change Institute, Centre for the Environment, University of Oxford, South Parks Road, Oxford OX1 3QY, UK
2Department of Atmospheric, Oceanic and Planetary Physics, Oxford University, Clarendon Laboratory, Parks Road, Oxford OX1 3PU, UK
3Centre for the Analysis of Time-series, Department of Statistics, Columbia House, London School of Economics and Political Science, Houghton Street, London WC2A 2AE, UK
Over the last 20 years, climate models have been developed to an impressive level of complexity. They are core tools in the study of the interactions of many climatic processes and justifiably provide an additional strand in the argument that anthropogenic climate change is a critical global problem. Over a similar period, there has been growing interest in the interpretation and probabilistic analysis of the output of computer models; particularly, models of natural systems. The results of these areas of research are being sought and utilized in the development of policy, in other academic disciplines, and more generally in societal decision making. Here, our focus is solely on complex climate models as predictive tools on decadal and longer time scales. We argue for a reassessment of the role of such models when used for this purpose and a reconsideration of strategies for model development and experimental design. Building on more generic work, we categorize sources of uncertainty as they relate to this specific problem and discuss experimental strategies available for their quantification. Complex climate models, as predictive tools for many variables and scales, cannot be meaningfully calibrated because they are simulating a never before experienced state of the system; the problem is one of extrapolation. It is therefore inappropriate to apply any of the currently available generic techniques which utilize observations to calibrate or weight models to produce forecast probabilities for the real world. To do so is misleading to the users of climate science in wider society. In this context, we discuss where we derive confidence in climate forecasts and present some concepts to aid discussion and communicate the state-of-the-art. Effective communication of the underlying assumptions and sources of forecast uncertainty is critical in the interaction between climate science, the impacts communities and society in general.