标题： mloz：一种用于气候敏感性模拟的高效机器学习臭氧参数化方法 显示英文标题

标题： mloz: A Highly Efficient Machine Learning-Based Ozone Parameterization for Climate Sensitivity Simulations

摘要： 大气臭氧是太阳辐射的重要吸收体，也是一种重要的温室气体。然而，大多数参与耦合模型比较项目（CMIP）的气候模型仍然缺乏对臭氧的交互表示，这是由于大气化学方案的计算成本较高。在这里，我们引入了一种机器学习参数化方法（mloz），以在标准气候敏感性模拟中交互地模拟对流层和平流层的日臭氧变化和趋势，包括臭氧与准两年振荡的双向相互作用。我们在十年时间尺度上证明了其高保真度，并且在两个不同的气候模型——英国地球系统模型（UKESM）和德国icosahedral非静力（ICON）模型中在线灵活使用。仅以大气温度廓线信息作为输入，mloz的臭氧预测速度比UKESM中的化学方案快约31倍，其贡献的总气候模型运行时间不到4%。特别是，我们还通过将参数化方法从UKESM转移到ICON，证明了其在没有化学方案的不同气候模型中的可转移性。这突显了在缺乏交互化学的CMIP级别气候模型中广泛采用的潜力，特别是在关注气候敏感性模拟时，臭氧趋势和变化已知会显著调节大气反馈过程。 显示英文摘要

摘要： Atmospheric ozone is a crucial absorber of solar radiation and an important greenhouse gas. However, most climate models participating in the Coupled Model Intercomparison Project (CMIP) still lack an interactive representation of ozone due to the high computational costs of atmospheric chemistry schemes. Here, we introduce a machine learning parameterization (mloz) to interactively model daily ozone variability and trends across the troposphere and stratosphere in standard climate sensitivity simulations, including two-way interactions of ozone with the Quasi-Biennial Oscillation. We demonstrate its high fidelity on decadal timescales and its flexible use online across two different climate models -- the UK Earth System Model (UKESM) and the German ICOsahedral Nonhydrostatic (ICON) model. With atmospheric temperature profile information as the only input, mloz produces stable ozone predictions around 31 times faster than the chemistry scheme in UKESM, contributing less than 4 percent of the respective total climate model runtimes. In particular, we also demonstrate its transferability to different climate models without chemistry schemes by transferring the parameterization from UKESM to ICON. This highlights the potential for widespread adoption in CMIP-level climate models that lack interactive chemistry for future climate change assessments, particularly when focusing on climate sensitivity simulations, where ozone trends and variability are known to significantly modulate atmospheric feedback processes.