Investigating state and timescale-dependency of climate variability using an isotope-enabled coupled general circulation model

目前尚不清楚全球和区域气候变异性是否会随着全球平均气温上升而增加或减少。对于社会来说，变异性变化是理解变化的关键，也是准备的代价高昂，因为它们导致极端事件的频率发生变化。鉴于此，该项目的目的是了解和量化平均变化和温度变化之间的联系。地球在最后一次冰盛期(21000年前)和中期全新世暖期(6000年前)的状态非常适合于这一目的，因为许多替代研究和模拟研究帮助描述了它们的平均状态气候。然而，即使到目前为止最先进、最协调的建模工作也不能用于面向可变性的模型数据比较。首先，已发表的模拟往往只有几个世纪长，接近于单个极地冰芯数据点通常所代表的情况。其次，自然强迫在这些气候状态下造成温度变化的作用尚不清楚，因为最先进的气候模拟是在没有改变太阳和火山强迫的情况下进行的。第三，过去温度变化的大部分高分辨率证据来自极地冰芯中稳定的同位素比率，其对温度的校准在很大程度上是不受限制的，特别是在南极洲。为了克服这些挑战，并系统地确定全球平均温度变化和变化变化之间的关系，将使用同位素启用的大气环流模型来模拟地球系统四个状态的集合轨迹。冰川状态将根据LGM进行建模，间冰期状态为中全新世。对于超冰期和超间冰期状态，通过改变大气二氧化碳浓度和大陆冰盖大小，全球气温分别进一步降低和增加。将进行两套千禧年模拟，一套是恒定的，另一套是变化的太阳和火山作用力。集合方法允许对长达千年的时间尺度进行时间序列分析，弥合代理和模型分辨率之间的差距。由于该模型明确地模拟了水的同位素特征，因此可以研究替代校准的平均状态依赖关系，并将其与大气和海洋动力学的变化联系起来。此外，比较有无强迫的实验将首次揭示自然强迫在不同气候状态下造成气候变异性的作用。同时，模型实验和替代数据将提供关于平均气温和温度变率变化之间关系的综合观点，从而有助于更好地理解未来气候变异性的潜在变化。

It is fundamentally unclear whether global and regional climate variability will increase or decrease along with global mean temperature rise. For societies, variability changes are crucial to understand, and expensive to prepare for, as they entail changes in the frequency of extreme events. In light of this, the aim of this project is to understand and quantify links between mean changes and variability in temperature.The Earth's state during the last Glacial Maximum (LGM, 21000 years ago) and the mid-Holocene warm period (6000 years ago) is well suited for this purpose, as many proxy and modeling studies have helped to characterize their mean state climates. However, even the most advanced, coordinated modeling efforts so far cannot be used for variability-oriented model-data comparison. Firstly, published simulations are often only a few centuries long, close to what a single polar ice core data point typically represents. Secondly, the role of natural forcing in creating temperature variability in these climate states is unknown, as the state-of-the-art climate simulations were run without varying solar and volcanic forcing. Thirdly, most high-resolution evidence for past temperature variability is derived from stable isotope ratios in polar ice cores, whose calibration to temperature is largely unconstrained, particularly for Antarctica.To overcome these challenges, and to identify relationships between global mean temperature changes and variability changes systematically, ensemble trajectories of four Earth system states will be simulated using an isotope-enabled general circulation model. A glacial state will be modeled according to the LGM, an interglacial state as the mid-Holocene. For a super-glacial and a super-interglacial state, global temperature is further reduced and increased, respectively, by changing the atmospheric CO2 concentrations and the continental ice-sheet size. Two suites of millennial simulations will be performed, one with constant, a second with varying solar and volcanic forcing.The ensemble approach allows time-series analyses on up to millennial timescales, bridging the gap between proxy and model resolution. Since the model explicitly simulates the isotopic signature of water, the mean-state dependence of the proxy calibration can be investigated, and linked to changes in atmospheric and oceanic dynamics. Furthermore, comparing the experiments with and without forcing will, for the first time, shed light on the role of natural forcing in creating climate variability in different climate states.Together, model experiments and proxy data will provide a comprehensive view on the relationship between mean temperature and temperature variability changes, thus contributing to a better understanding of potential changes to future climate variability.