Lehner Flavio
Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, NY, USA.
Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, CO, USA.
iScience. 2024 Oct 9;27(11):111113. doi: 10.1016/j.isci.2024.111113. eCollection 2024 Nov 15.
Some of the most impactful climate and weather events result from compounding drivers. To robustly assess the current and future risk from such compound events, a better understanding of the associated sources of uncertainty is needed. Internal variability confounds detection and attribution of human-induced climate change and imposes irreducible limits on the accuracy of climate projections. Response uncertainty can lead to divergent projections for many societally important quantities such as precipitation. Combined with unknown future greenhouse gas emissions, these uncertainties can result in a socio-economically paralyzing range of future storylines. Climate model large ensembles are uniquely positioned to assess these uncertainties and are rightfully gaining popularity in compound event research, but they need to be accompanied by rigorous model validation and robust observational constraints to reach their full potential in terms of usefulness for practitioners. This perspective discusses these opportunities and challenges at the example of water resources and provides an outlook on application-oriented compound event research with large ensembles.
一些最具影响力的气候和天气事件是由多种驱动因素共同作用导致的。为了稳健地评估此类复合事件当前和未来的风险，需要更好地理解相关的不确定性来源。内部变率混淆了人为引起的气候变化的检测和归因，并对气候预测的准确性施加了不可减少的限制。响应不确定性可能导致对许多对社会重要的量（如降水量）的预测出现分歧。再加上未来温室气体排放情况不明，这些不确定性可能导致一系列在社会经济层面令人陷入瘫痪的未来情景。气候模式大集合在评估这些不确定性方面具有独特优势，并且在复合事件研究中越来越受到欢迎，但它们需要辅以严格的模型验证和强有力的观测约束，才能在对实际工作者的有用性方面充分发挥其潜力。本视角以水资源为例讨论了这些机遇与挑战，并展望了面向应用的大集合复合事件研究。
