Climate response metrics are used to quantify the Earth's climate response to anthropogenic changes of atmospheric CO2. Equilibrium Climate Sensitivity (ECS) is one such metric that measures the equilibrium response to CO2 doubling. However, both in their estimation and their usage, such metrics make assumptions on the linearity of climate response, although it is known that, especially for larger forcing levels, response can be nonlinear. Such nonlinear responses may become visible immediately in response to a larger perturbation, or may only become apparent after a long transient. In this paper, we illustrate some potential problems and caveats when estimating ECS from transient simulations. We highlight ways that very slow timescales may lead to poor estimation of ECS even if there is seemingly good fit to linear response over moderate timescales. Moreover, such slow timescale might lead to late abrupt responses ("late tipping points") associated with a system's nonlinearities. We illustrate these ideas using simulations on a global energy balance model with dynamic albedo. We also discuss the implications for estimating ECS for global climate models, highlighting that it is likely to remain difficult to make definitive statements about the simulation times needed to reach an equilibrium.
ClimTip-GML: A global bias-corrected and downscaled dataset for assessing impacts of climate tipping events
Technical University of Munich（慕尼黑工业大学）；Potsdam Institute for Climate Impact Research（波茨坦气候影响研究所）；Ludwig-Maximilians University of Munich（慕尼黑路德维希-马克西米利安大学）；Met Office（英国气象局）；Utrecht University（乌得勒支大学）；University of Exeter（埃克塞特大学）
Accelerating exploration of Marine Cloud Brightening impacts on tipping points Using an AI Implementation of Fluctuation-Dissipation Theorem
University of Victoria（维多利亚大学）；Palo Alto Research Center（帕洛阿尔托研究中心）；Excarta；University of California San Diego（加利福尼亚大学圣迭戈分校）；University of Washington（华盛顿大学）
A stable hothouse triggered by a tipping mechanism
Brevan Howard Centre for Financial Analysis, Imperial College Business School, Imperial College London（帝国理工学院商业学院布雷文·霍华德金融分析中心）；Laboratoire de Météorologie Dynamique (CNRS & IPSL), Ecole Polytechnique（巴黎综合理工学院动力气象学实验室（法国国家科学研究中心与IPSL））；Unit of Theoretical Chronobiology, Faculty of Sciences, Université Libre de Bruxelles（布鲁塞尔自由大学理学院理论时间生物学单元）；Geosciences Department and Laboratoire de Météorologie Dynamique (CNRS & IPSL), Ecole Normale Supérieure and PSL University（巴黎高等师范学院和PSL大学地质科学系及动力气象学实验室（法国国家科学研究中心与IPSL））；Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles（加州大学洛杉矶分校大气与海洋科学系）；Department of Mathematics, Imperial College London（帝国理工学院数学系）
Quantifying the safe operating space for the Amazon rainforest under climate change and deforestation
Potsdam Institute for Climate Impact Research（波茨坦气候影响研究所）；Utrecht University（乌得勒支大学）；Stockholm University（斯德哥尔摩大学）；Max Planck Institute of Geoanthropology（马克斯·普朗克人类geoanthropology研究所）；University of Potsdam（波茨坦大学）；Goethe University Frankfurt（法兰克福歌德大学）；Senckenberg Research Institute and Natural History Museum（森根堡自然历史博物馆研究学院）
Planetary climate interactions of the Qinghai-Tibetan Plateau
Beijing Normal University（北京师范大学）；Technical University of Munich（慕尼黑工业大学）；Potsdam Institute for Climate Impact Research（波茨坦气候影响研究所）；Institute of Atmospheric Physics, Chinese Academy of Sciences（中国科学院大气物理研究所）；Humboldt University（洪堡大学）；Bar-Ilan University（巴伊兰大学）；Institute of Tibetan Plateau Research, Chinese Academy of Sciences（中国科学院青藏高原研究所）；Tsinghua University（清华大学）
Constructing efficient score functions for rare event simulation in high-dimensional ocean-climate models
Institute for Marine and Atmospheric research Utrecht, Department of Physics, Utrecht University（乌得勒支大学海洋与大气研究所物理系）；Utrecht University（乌得勒支大学）；Netherlands eScience Center（荷兰电子科学中心）；IMEC；Centre for Complex Systems Studies, Department of Physics, Utrecht University（乌得勒支大学复杂系统研究中心物理系）
Climate network characterization of the AMOC edge state
University of Geneva（日内瓦大学）；Centre pour la Vie dans l’Univers (CVU)（宇宙生命中心）；Institute for Marine and Atmospheric research Utrecht（乌得勒支海洋与大气研究所）；Utrecht University（乌得勒支大学）；School of Computing and Mathematical Sciences, University of Leicester（莱斯特大学计算与数学科学学院）；Schools of Sciences, Great Bay University（大湾区大学理学院）
Machine-learning prediction of tipping with applications to the Atlantic Meridional Overturning Circulation
School of Electrical, Computer, and Energy Engineering, Arizona State University（亚利桑那州立大学电气、计算机与能源工程学院）；Army Research Directorate, DEVCOM Army Research Laboratory（DEVCOM陆军研究实验室陆军研究局）；Department of Physics, Arizona State University（亚利桑那州立大学物理系）
Quantification of the cascading tipping probability from the AMOC to the Amazon rainforest
Institute for Marine and Atmospheric research Utrecht, Department of Physics, Utrecht University（乌得勒支大学海洋与大气研究所物理系）；Center for Complex Systems Studies, Department of Physics, Utrecht University（乌得勒支大学复杂系统研究中心物理系）
Semi-automatic tuning of coupled climate models with multiple intrinsic timescales: lessons learned from the Lorenz96 model
Institut Pierre-Simon Laplace（皮埃尔-西蒙·拉普拉斯研究所）；IRD（法国发展研究院）；Sorbonne Université（索邦大学）；LOCEAN-IPSL（LOCEAN-拉普拉斯研究所）；CNRS（法国国家科学研究中心）；Laboratoire des Sciences du Climat et de l’Environnement（气候与环境科学实验室）；CEA Saclay（法国原子能委员会萨克雷中心）；Princeton University（普林斯顿大学）；NOAA/Geophysical Fluid Dynamics Laboratory（美国国家海洋和大气管理局地球物理流体动力学实验室）
Probing the timescale dependency of local and global variations in surface air temperature from climate simulations and reconstructions of the last millennia
