Reversible Record Breaking and Variability: Temperature Distributions across the Globe
Reversible Record Breaking and Variability: Temperature Distributions across the Globe
复制标题
DOI：
10.1175/2010jamc2407.1
复制
发表时间：
2010-08-01
影响因子：
3
通讯作者：
Kostinski, Alexander
中科院分区：
文献类型：
--
作者：
Anderson, Amalia;Kostinski, Alexander
基于历史最高和最低点的计数，并采用时间可逆性，提出了一种方法来检查自然变率。重点是内在变异性;也就是说，方差与平均值的趋势分离。本文提出并研究了一个适用于地球仪月温度时间序列集合的变率指数α。对于时间序列的集合，（平均α）与零的偏差表示以独立于分布的方式的方差趋势。对于来自不同地理位置的15635个月温度时间序列（全球历史气候学网络），每个时间序列约一个世纪长，=-1.0，表明变率下降。该值比静态模拟的3 sigma值大一个数量级。使用传统的最佳拟合高斯温度分布，这种趋势与年际月平均温度分布的标准差（约10%）的变化有关，约为-0.2摄氏度（106年）（1）。
Based on counts of record highs and lows, and employing reversibility in time, an approach to examining natural variability is proposed. The focus is on intrinsic variability; that is, variance separated from the trend in the mean. A variability index alpha is suggested and studied for an ensemble of monthly temperature time series around the globe. Deviation of (mean alpha) from zero, for an ensemble of time series, signifies a variance trend in a distribution-independent manner. For 15 635 monthly temperature time series from different geographical locations (Global Historical Climatology Network), each time series about a century-long, = -1.0, indicating decreasing variability. This value is an order of magnitude greater than the 3 sigma value of stationary simulations. Using the conventional best-fit Gaussian temperature distribution, the trend is associated with a change of about -0.2 degrees C (106 yr) (1) in the standard deviation of interannual monthly mean temperature distributions (about 10%).