Unifided approach of climate modeling and satellite data analysis toward reducing uncertainty of climate change projection

即使在最新的IPCC评估报告中，对未来全球平均气温上升的预测也包含了不确定性。这种不确定性部分归因于全球气候模式（GCMs）参数化过程（特别是云）的潜在误差。因此，本研究试图通过生成一个模式参数和参数化方案受到扰动的大型集合来澄清云对气候变暖反馈的不确定性来源。此外，云特性的卫星数据也被积极用于评估气候模式模拟。基于MIROC GCM的物理集合是一套探索云反馈过程的新型数值实验，已成功得到国际气候科学界的认可。比较GCM和卫星资料之间的平均云和云宏观物理特性，可以为后续气候变化预测提供改进GCM的线索。

Future projection of global mean temperature rise has contained uncertainty, even in the latest IPCC Assessment Report. This uncertainty is partly attributed to potential errors in parameterization processes (of clouds in particular) in global climate models (GCMs). Therefore, this study attempted to clarify sources of uncertainty in cloud feedbacks to climate warming by generating a large ensemble in which model parameters as well as parameterization schemes have been perturbed. Also, satellite data for cloud properties were actively used to assess climate model simulations.Physics ensemble based on the MIROC GCM is a novel set of numerical experiments to explore the cloud feedback processes, and was successfully recognised in international climate science community. Comparisons of mean cloud and cloud macrophysical properties between GCM and satellite data could prodive clues for improving GCM for the subsequent climate change projection.