It's statistical polling...for science.
A paper in Nature Climate Change uses structured expert elicitation and mathematically pools experts' opinions to forecast future sea level rises from melting ice sheets. Soliciting and pooling expert judgments is used in eruption forecasting and the spread of vector borne diseases - with questionable accuracy - and in their paper Professor Jonathan Bamber and Professor Willy Aspinall from the University of Bristol try to model the uncertainties in the future response of the ice sheets.
The ice sheets covering Antarctica and Greenland contain about 99.5 percent of the Earth's glacier ice, they would raise global sea level by over 60 meters if they were to melt completely (it's not happening, relax) and so any melting of ice sheets is considered the largest potential source of future sea level rise. That is where there is also the largest uncertainty so predicting their future response using numerical modeling is part science and part magic.
Structured expert elicitation found that the median estimate for the sea level contribution from the ice sheets by 2100 was 29 cm with a 5 percent probability that it could exceed 84 cm. When combined with other sources of sea level rise, the authors state a conceivable risk of a rise of greater than one meter by 2100. The IPCC's last report estimated 18-59 cm for six possible scenarios so 100 cm is obviously aggressive, but that is why they use the term 'conceivable'.
Scientists, as a group, are highly uncertain about the cause of the recent increase in ice sheet mass loss observed by satellites, the modelers note, and equally unsure whether this is part of a long term trend or due to short-term fluctuations in the climate system.
Bamber said: "This is the first study of its kind on ice sheet melting to use a formalized mathematical pooling of experts' opinions. It demonstrates the value and potential of this approach for a wide range of similar problems in climate change research, where past data and current numerical modelling have significant limitations when it comes to forecasting future trends and patterns."
