根据气候模拟和上千年重建探测地面air temperature局部和全球变化的时间尺度依赖性
Probing the timescale dependency of local and global variations in surface air temperature from climate simulations and reconstructions of the last millennia
摘要：
地球的气候可以被理解为一个动力系统，由于外部强迫和内部耦合而发生变化。基本的气候变量，如地表气温，描述了这种动态。我们目前的间冰期，全新世（11,700年前到今天），其特征是在人为变暖之前全球平均温度的微小变化。然而，围绕这一平均值的波动机制和时空模式，即所谓的温度变率，尽管具有社会经济意义，但人们对此知之甚少。在这里，我们检查温度变化模型模拟和古气候重建的分类本地和全球地表气温的标度行为的时间尺度上几年到几个世纪之间的差异。为此，我们对比功率谱密度（PSD）和他们的幂律缩放模拟和观测为基础的温度系列的过去6000年。我们进一步引入光谱增益来解开外部强迫和内部产生的变化作为时间尺度的函数。它是基于我们的估计的联合PSD的辐射强迫，其中表现出一个尺度突变的周期约为7年。我们发现，来自古气候重建的局部温度序列显示出与模拟的不同的标度行为，具有更强的持久性（即，时间序列内连续值之间的相关性）。相反，全球平均温度的PSD和光谱增益在数据集之间是一致的。我们的研究结果指出了气候模式的局限性，以充分代表当地的温度统计几十年到几个世纪。通过强调温度变率的关键特征，我们为更好地限制全球变暖导致的温度变率可能发生的变化和评估未来的气候风险铺平了道路。
英文摘要：
Earth's climate can be understood as a dynamical system that changes due to external forcing and internal couplings. Essential climate variables, such as surface air temperature, describe this dynamics. Our current interglacial, the Holocene (11,700 yr ago to today), has been characterized by small variations in global mean temperature prior to anthropogenic warming. However, the mechanisms and spatiotemporal patterns of fluctuations around this mean, called temperature variability, are poorly understood despite their socio-economic relevance. Here, we examine discrepancies between temperature variability from model simulations and paleoclimate reconstructions by categorizing the scaling behavior of local and global surface air temperature on the timescale of years to centuries. To this end, we contrast power spectral densities (PSD) and their power-law scaling using simulated and observation-based temperature series of the last 6000 yr. We further introduce the spectral gain to disentangle the externally forced and internally generated variability as a function of timescale. It is based on our estimate of the joint PSD of radiative forcing, which exhibits a scale break around the period of 7 yr. We find that local temperature series from paleoclimate reconstructions show a different scaling behavior than simulated ones, with a tendency towards stronger persistence (i.e., correlation between successive values within a time series) on periods of 10 to 200 yr. Conversely, the PSD and spectral gain of global mean temperature are consistent across data sets. Our results point to the limitation of climate models to fully represent local temperature statistics over decades to centuries. By highlighting the key characteristics of temperature variability, we pave a way to better constrain possible changes in temperature variability with global warming and assess future climate risks.
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