标题： 瞬态气候中的温度：改进的随时间演变协方差的模拟方法 显示英文标题

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

摘要： 未来气候变化的影响不仅取决于温度均值的变化，还取决于温度变异性变化。 通用环流模型（GCMs）预测了均值和变异性两方面的变化；然而，GCM输出不能直接用作影响评估的模拟结果，因为GCM未能完全再现当前的温度分布。 本文解决了一个需要结合观测记录和GCM对均值和时间协方差变化的预测的未来温度模拟问题。 我们的观点是，这种模拟应基于将观测数据转换以考虑GCM预测的变化，而不是将GCM输出转换以考虑与观测的差异。 我们的方法旨在模拟瞬态（非平稳）气候，这些气候正在响应CO$_2$浓度的变化（正如目前的地球一样）。 这项工作建立在之前描述的用于模拟平衡（平稳）气候的方法之上。 由于所提出的模拟依赖于GCM预测的协方差变化，我们描述了一个统计模型，用于描述在未来的强迫情景下GCM中时间协方差的演变，并将该模型应用于一个GCM（CCSM3）的集合运行。 我们发现，在至少CCSM3中，局部协方差结构的变化可以作为温度区域均值变化和升温速率的函数来解释。 这一特性意味着统计模型可以用来模拟在GCM未运行的情景下GCM温度的演变协方差结构。 当与均温的模拟器结合使用时，我们的方法可以在一种考虑变化预测的同时仍保持与观测记录一致的方式下，模拟此类情景下的演变温度。 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.