Japan Geoscience Union Meeting 2016

Presentation information


Symbol A (Atmospheric and Hydrospheric Sciences) » A-HW Hydrology & Water Environment

[A-HW17] Hydrological Cycle and Water Environment

Wed. May 25, 2016 5:15 PM - 6:30 PM Poster Hall (International Exhibition Hall HALL6)

Convener:*Atsushi Higuchi(Center for Environmental Remote Sensing (CEReS), Chiba University, Japan), Seiya Nagao(Institute of Nature and Environmental Technology, Kanazawa University), Takeshi Hayashi(Faculty of Education and Human Studies, Akita University), Youhei Uchida(Geological Survey of Japan, AIST)

5:15 PM - 6:30 PM

[AHW17-P06] Predicting future uncertainty constraints on global warming projections

*Hideo Shiogama1, Daithi Stone2, Seita Emori1, Kiyoshi Takahashi1, Shunsuke Mori3, Akira Maeda4, Myles Allen5 (1.National Institute for Environmental Studies, 2.Lawrence Berkeley National Laboratory, 3.Tokyo University of Science, 4.The University of Tokyo, 5.University of Oxford)

Keywords:climate change, climate change projection

Projections of global mean temperature changes (dT) in the future are associated with intrinsic uncertainties. Much climate policy discourse has been guided by “current knowledge” of the dTs uncertainty, ignoring the likely future reductions of the uncertainty, because a mechanism for predicting these reductions is lacking. By using simulations of Global Climate Models from the Coupled Model Intercomparison Project Phase 5 ensemble as pseudo past and future observations, we estimate how fast and in what way the uncertainties of dT can decline when the current observation network of surface air temperature is maintained. At least in the world of pseudo observations under the Representative Concentration Pathways (RCPs), we can drastically reduce more than 50% of the dTs uncertainty in the 2040s by 2029, and more than 60% of the dTs uncertainty in the 2090s by 2049. Under the highest forcing scenario of RCPs, we can predict the true timing of passing the 2°C (3°C) warming threshold 20 (30) years in advance with errors less than 10 years. These results demonstrate potential for sequential decision-making strategies to take advantage of future progress in understanding of anthropogenic climate change.