Washington, Jan 23 : Climate scientists are collaborating with economists to improve their forecasting models for global warming by assessing more accurately the impact of rising atmospheric carbon dioxide levels.
Although there is broad consensus that there will be a significant rise in average global temperature, there is great uncertainty over the extent of the change, and the implications for different regions.
Greater accuracy is urgently needed to provide a sound basis for major policy decisions and to ensure that politicians and the public remain convinced that significant changes in consumption patterns and energy production are essential to stave off serious consequences in the coming decades and centuries.
The climate modelling community has become increasingly aware that some of the statistical tools that could improve their modelling of climate change may already have been developed for econometric problems, which have some of the same features. he European Science Foundation (ESF) brought these two communities together for the first time in a recent workshop, sowing the seeds for future collaboration.
"We achieved our goal of bringing together people from two very distant but equally valuable fields," said the workshop's co-convenor Peter Thejll. "It was designed as a one-way session whereby econometricians were supposed to convey knowledge of econometric methods to the climate researchers," he added.
This has already proved highly valuable because economic and climate models require similar kinds of statistical analysis, both for example involving serial correlation where the aim is to predict the future value of a variable based upon a starting value at an earlier point in time.
In economics, such a variable might be the price of a commodity, while in climatology it might be temperature or atmospheric pressure.
In both cases, the variables change randomly during successive time intervals subject to varying constraints within a closely defined zone, and therefore can be analysed using similar "random walk" techniques.
"To solve important climate problems related to climate change and change attribution with statistics, these methods have to be used and understood by climate researchers," said Thejll. "We brought together people who understand these problems and had a great, and informative time," he added.
But first, the aim is to introduce greater statistical sophistication into climate analysis, partly by understanding better the correlation between different aspects of change, for example how one region impacts another.
"We first need to see the spread of econometric methods so that we no longer read climate research papers that ignore important statistical problems," said Thejll.