LAUSR

Models disagree on a significant number of responses to climate change, such as climate feedback, regional changes, or the strength of equilibrium climate sensitivity. Emergent constraints aim to reduce these uncertainties by finding links between the inter-model spread in an observable predictor and climate projections. In this paper, the concepts underlying this framework are recalled with an emphasis on the statistical inference used for narrowing uncertainties, and a review of emergent constraints found in the last two decades. Potential links between highlighted predictors are explored, especially those targeting uncertainty reductions in climate sensitivity, cloud feedback, and changes of the hydrological cycle. Yet the disagreement across emergent constraints suggests that the spread in climate sensitivity can not be significantly narrowed. This calls for weighting the realism of emergent constraints by quantifying the level of physical understanding explaining the relationship. This would also permit more efficient model evaluation and better targeted model development. In the context of the upcoming CMIP6 model intercomparison a growing number of new predictors and uncertainty reductions is expected, which call for robust statistical inferences that allow cross-validation of more likely estimates.摘要很多模式在对气候变化的很多响应上存在分歧,如气候反馈、区域变化、气候平衡态敏感性的强度等。观测涌现约束方法(以下称为“涌现约束”)是找到关于预报因子的多模式间预报离散性和气候预测的关系,来减少模式间预报的不确定性。本文回顾了在涌现约束这个框架下的一些基本概念,强调了用于缩小不确定性的统计推断,此外文章还对过去二十年间关于涌现约束的研究做了回顾。本文探索了重要因子之间的潜在联系,尤其是尝试缩小气候敏感度、云反馈和水循环的变化的不确定性。但涌现约束结果的不统一表明目前的方法还不能显著地缩小气候敏感度的不确定性范围。这要求通过对这个关系的物理理解的水平进行量化来加强涌现约束真实性。这可能也需要更有效率的模式评估和更有针对性地发展模式。在即将到来的CMIP6模式间比较中,我们希望看到更多的预报因子并且希望预报不确定性能缩小,这需要可靠的统计推断,以便对更有可能的估计进行交叉验证。