The aim of paleoclimate studies resolving climate variability from noisy proxy records can in essence be reduced to a statistical problem. The challenge is to extract meaningful information about climate variability from these records by reducing measurement uncertainty through combining measurements for proxies while retaining the temporal resolution needed to assess the timing and duration of variations in climate parameters. In this study, we explore the limits of this compromise by testing different methods for combining proxy data (smoothing, binning, and sample size optimization) on a particularly challenging paleoclimate problem: resolving seasonal variability in stable isotope records. We test and evaluate the effects of changes in the seasonal temperature and the hydrological cycle as well as changes in the accretion rate of the archive and parameters such as sampling resolution and age model uncertainty in the reliability of seasonality reconstructions based on clumped and oxygen isotope analyses in 33 real and virtual datasets. Our results show that strategic combinations of clumped isotope analyses can significantly improve the accuracy of seasonality reconstructions compared to conventional stable oxygen isotope analyses, especially in settings in which the isotopic composition of the water is poorly constrained. Smoothing data using a moving average often leads to an apparent dampening of the seasonal cycle, significantly reducing the accuracy of reconstructions. A statistical sample size optimization protocol yields more precise results than smoothing. However, the most accurate results are obtained through monthly binning of proxy data, especially in cases in which growth rate or water composition cycles obscure the seasonal temperature cycle. Our analysis of a wide range of natural situations reveals that the effect of temperature seasonality on oxygen isotope records almost invariably exceeds that of changes in water composition. Thus, in most cases, oxygen isotope records allow reliable identification of growth seasonality as a basis for age modeling in the absence of independent chronological markers in the record. These specific findings allow us to formulate general recommendations for sampling and combining data in paleoclimate research and have implications beyond the reconstruction of seasonality. We briefly discuss the implications of our results for solving common problems in paleoclimatology and stratigraphy.

中文翻译：

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优化高分辨率古气候记录中的采样策略

古气候研究从嘈杂的替代记录中解决气候变异的目的本质上可以归结为一个统计问题。面临的挑战是通过组合代理测量来降低测量不确定性，同时保留评估气候参数变化的时间和持续时间所需的时间分辨率，从而从这些记录中提取有关气候变率的有意义的信息。在这项研究中，我们通过在一个特别具有挑战性的古气候问题上测试不同的代理数据组合方法（平滑、分箱和样本大小优化）来探索这种妥协的局限性：解决稳定同位素记录中的季节性变化。我们测试和评估季节性温度和水文循环变化以及档案吸积率变化的影响，以及采样分辨率和年龄​​模型不确定性等参数对基于聚集和氧同位素分析的季节性重建可靠性的影响在 33 个真实和虚拟数据集中。我们的结果表明，与传统的稳定氧同位素分析相比，丛集同位素分析的战略组合可以显着提高季节性重建的准确性，尤其是在水的同位素组成不受严格限制的情况下。使用移动平均值平滑数据通常会导致季节性周期明显减弱，从而显着降低重建的准确性。统计样本大小优化协议比平滑产生更精确的结果。然而，最准确的结果是通过代理数据的每月分箱获得的，特别是在增长率或水成分循环掩盖了季节性温度循环的情况下。我们对各种自然情况的分析表明，温度季节性对氧同位素记录的影响几乎总是超过水成分变化的影响。因此，在大多数情况下，氧同位素记录允许可靠地识别生长季节性，作为年龄建模的基础，而记录中没有独立的年代标记。这些具体的发现使我们能够为古气候研究中的采样和数据组合制定一般建议，并具有超越季节性重建的意义。我们简要讨论了我们的结果对解决古气候学和地层学中常见问题的意义。