在短期和长期相关性存在的情况下进行趋势分析,并将其应用于区域变暖
Ewan T Phillips1, Marc Höll1, Holger Kantz1
1Max Planck Institute for the Physics of Complex Systems, Nöthnitzer Str. 38, 01187 Dresden, Germany.
Physical review. E
|October 18, 2023
概括
本研究引入了一个新的框架,用于分析时间序列数据的趋势,考虑短期和长期的时间相关性. 这项研究揭示了过去70年中欧地区的显著变暖趋势,在过去二十年中加速.
科学领域:
- 时间序列分析时间序列分析
- 气候科学 气候科学
- 统计建模 统计建模
背景情况:
- 现实世界的时间序列经常显示短期和长期的时间相关性.
- 这些相关性可以显著影响线性趋势分析的准确性.
- 现有的方法可能无法充分解决这些双重相关性带来的复杂性.
研究的目的:
- 开发一个趋势分析的一般框架,包括短期和长期的时间相关性.
- 为这种复杂的时间序列提供趋势估计中的错误条的分析结果.
- 应用这个框架来研究格式温度数据中的变暖趋势.
主要方法:
- 提出了一个节的模型,具有短距离的自回归和长距离的分数参数.
- 对于最小正方形趋势估计的错误条的衍生闭式结果.
- 采用集体方法进行缩放区域提取和格鲁诺瓦尔德-莱特尼科夫导数进行参数估计.
- 利用空间平均和经验直角函数分析用于格式温度数据.
主要成果:
- 突出了短期和长期相关性对趋势分析错误条的明显影响.
- 成功估计了分数和自回归参数及其错误条.
- 在空间平均和主要组件分析方法之间发现了良好的一致性.
- 在过去的70年中,在中欧确定了统计学上显著的十年升温趋势.
结论:
- 开发的框架有效地分析了时间序列中的趋势,具有复杂的时间相关性.
- 在过去的70年中,中欧经历了显著的变暖趋势.
- 在最近20年中,气候变暖的速度急剧增加,这表明气候变化信号正在加速.
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