全球空气温度异常时间序列中的多个序列相关性
Meng Gao1, Xiaoyu Fang1, Ruijun Ge1
1School of Mathematics and Information Sciences, Yantai University, Yantai, China.
PloS one
|July 9, 2024
概括
在全球表面空气温度 (SAT) 时间序列中,序列相关性是普遍的. 了解这些时间模式,包括短期,长期和非线性类型,对于气候科学研究至关重要.
科学领域:
- 气候科学 气候科学
- 时间序列分析时间序列分析
- 统计物理 统计物理
背景情况:
- 温度数据中的序列相关性表明气候事件的时间一致性.
- 全球表面空气温度 (SAT) 时间序列包含了对气候系统理解至关重要的固有变化.
研究的目的:
- 为了研究全球表面空气温度 (SAT) 异常时间序列中的序列相关性.
- 识别和描述气候数据中的短期,长期和非线性序列相关性.
主要方法:
- 预处理SAT时间序列以获取异常数据.
- 使用一级自回归模型 (AR(1) 进行短期相关.
- 对于长期相关性,应用延迟波动分析 (DFA).
- 使用水平可见度图 (HVG) 算法和一个新的参数 (Δσ) 进行非线性相关.
- 使用蒙特卡洛模拟进行统计显著性测试.
主要成果:
- 确定了与大气现象 (如罗斯比波) 相关的短期关联的全球模式.
- 检测到与气候变异性相一致的长期相关性,例如厄尔尼诺-南方振荡 (ENSO).
- 证明了HVG拓参数和Δσ在捕获和检测时间相关性的有效性.
结论:
- 序列相关性是全球SAT时间序列的普遍特征.
- 鉴定的相关性类型为气候动态和变化提供了洞察力.
- 这些发现强调了在气候科学分析中考虑序列相关性的重要性.
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