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使气候预测以历史观测为条件。

Making climate projections conditional on historical observations.

作者信息

Ribes Aurélien, Qasmi Saïd, Gillett Nathan P

机构信息

CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France.

CCCMA, Environment and Climate Change Canada, Victoria, BC, Canada.

出版信息

Sci Adv. 2021 Jan 22;7(4). doi: 10.1126/sciadv.abc0671. Print 2021 Jan.

Abstract

Many studies have sought to constrain climate projections based on recent observations. Until recently, these constraints had limited impact, and projected warming ranges were driven primarily by model outputs. Here, we use the newest climate model ensemble, improved observations, and a new statistical method to narrow uncertainty on estimates of past and future human-induced warming. Cross-validation suggests that our method produces robust results and is not overconfident. We derive consistent observationally constrained estimates of attributable warming to date and warming rate, the response to a range of future scenarios, and metrics of climate sensitivity. We find that historical observations narrow uncertainty on projected future warming by about 50%. Our results suggest that using an unconstrained multimodel ensemble is no longer the best choice for global mean temperature projections and that the lower end of previous estimates of 21st century warming can now be excluded.

摘要

许多研究试图根据近期观测结果来限制气候预测。直到最近,这些限制的影响有限,预测的变暖范围主要由模型输出驱动。在此,我们使用最新的气候模型集合、改进的观测数据以及一种新的统计方法来缩小对过去和未来人为导致变暖估计的不确定性。交叉验证表明我们的方法产生了可靠的结果且不过于自信。我们得出了关于迄今可归因变暖、变暖速率、对一系列未来情景的响应以及气候敏感性指标的一致的受观测约束估计。我们发现历史观测将预测未来变暖的不确定性缩小了约50%。我们的结果表明,对于全球平均温度预测而言,使用无约束的多模型集合不再是最佳选择,并且此前21世纪变暖估计范围的下限现在可以排除。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6c1/10670938/b35639496e8f/abc0671-F1.jpg

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