标题： 瞬变气候中的温度：具有演化时间协方差的模拟改进方法 显示英文标题

标题： Temperatures in transient climates: improved methods for simulations with evolving temporal covariances

摘要： 未来的气候变化影响不仅取决于温度的变化均值，还取决于其变异性变化。一般环流模型（GCMs）预测了均值和变异性两方面的变化；然而，由于GCMs未能完全再现当前的温度分布，因此其输出不应直接用于影响评估的模拟。本文旨在解决未来温度模拟的需求，结合观测记录与GCM对均值和时间协方差变化的预测。我们的观点是，此类模拟应基于将观测数据转换以反映GCM预测的变化，而非通过调整GCM输出来弥补与观测数据的差异。我们的方法设计用于模拟瞬态（非平稳）气候，这种气候正在响应CO$_2$浓度的变化而演变（正如当今地球一样）。这项工作建立在之前描述的平衡（平稳）气候模拟方法的基础上。由于所提出的模拟依赖于GCM对未来强迫情景下协方差变化的预测，我们描述了一个统计模型来模拟GCM中时间协方差的演变，并将其应用于来自一个GCM（即CCSM3）的一组运行结果。我们发现，在CCSM3中，局部协方差结构的变化可以解释为区域平均温度变化及其升温速率变化的函数。这一特性意味着该统计模型可用于模拟GCM在未运行的情景下温度的演变协方差结构。当结合均值温度的仿真器时，我们的方法能够在这种情景下模拟出随时间演变的温度，同时考虑变化的预测，但仍保留与观测记录的一致性。 显示英文摘要

摘要： Future climate change impacts depend on temperatures not only through changes in their means but also through changes in their variability. General circulation models (GCMs) predict changes in both means and variability; however, GCM output should not be used directly as simulations for impacts assessments because GCMs do not fully reproduce present-day temperature distributions. This paper addresses an ensuing need for simulations of future temperatures that combine both the observational record and GCM projections of changes in means and temporal covariances. Our perspective is that such simulations should be based on transforming observations to account for GCM projected changes, in contrast to methods that transform GCM output to account for discrepancies with observations. Our methodology is designed for simulating transient (non-stationary) climates, which are evolving in response to changes in CO$_2$ concentrations (as is the Earth at present). This work builds on previously described methods for simulating equilibrium (stationary) climates. Since the proposed simulation relies on GCM projected changes in covariance, we describe a statistical model for the evolution of temporal covariances in a GCM under future forcing scenarios, and apply this model to an ensemble of runs from one GCM, CCSM3. We find that, at least in CCSM3, changes in the local covariance structure can be explained as a function of the regional mean change in temperature and the rate of change of warming. This feature means that the statistical model can be used to emulate the evolving covariance structure of GCM temperatures under scenarios for which the GCM has not been run. When combined with an emulator for mean temperature, our methodology can simulate evolving temperatures under such scenarios, in a way that accounts for projections of changes while still retaining fidelity with the observational record.