针对复杂动力系统模型中稳定性偏见的校准
Targeted Calibration to Adjust Stability Biases in Complex Dynamical System Models
AI总结：
本文提出一种高效校准方法，用于调整复杂动力系统模型的稳定性偏见，以研究地球系统模型中的临界转变可能性及影响。
摘要：
复杂动力系统模型（如地球气候模型）通常包含大量不确定参数，全面探索参数空间因计算成本过高而不可行。系统梯度基参数优化不可行，因为此类模型通常不可微。这在模型描述高度非线性和可能突发动态时尤为突出，参数变化敏感性高。地球系统组成部分如大西洋环流或极地冰盖可能因人为气候变化而发生临界转变。有观点认为，目前最先进的地球系统模型对这些组件过于稳定。在本报告中，将展示新情景模拟如何研究地球系统模型中临界事件的可能性及后果，并讨论一种高效、系统且客观的校准方法，用于调整复杂系统模型的稳定性。给定物理或观测约束，该方法能有效调整复杂系统模型中的稳定性偏见，帮助揭示潜在隐藏的不稳定性，对地球系统建模有重要影响。
英文摘要：
Models of complex dynamical systems like the Earth's climate often involve large numbers of uncertain parameters. Comprehensive exploration of the parameter space is typically prohibitive due to excessive computational costs. Systematic gradient-based parameter optimization is not feasible because such models are typically not differentiable. This is especially problematic in cases where the models describe highly nonlinear and possibly abrupt dynamics, where sensitivity to parameter changes is high. Components of Earth's climate system, such as the North Atlantic Overturning Circulation or the polar ice sheets, are at risk of undergoing critical transitions in response to anthropogenic climate change. Concerns have been raised that these Earth system components are too stable in state-of-the-art models. In my presentation, we will see examples how new scenario simulations allow studying the possibility and the consequences of tipping events in Earth system models. Also, we will discuss a method for efficient, systematic, and objective calibration of dynamical complex system models, targeted at adjusting system stability. Given a number of physical or observational constraints, the method can efficiently adjust stability biases in a range of complex system models and help reveal potentially hidden instabilities, with important implications for Earth system modelling.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
