Trends in moments of climatic indices
Trends in moments of climatic indices
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DOI：
10.1029/2001gl014025
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发表时间：
2002-01
影响因子：
5.2
通讯作者：
K. Vinnikov;A. Robock
中科院分区：
文献类型：
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作者：
K. Vinnikov;A. Robock
我们提出一种新技术来分析气候指数统计分布矩的趋势。首先使用一种标准方法（线性回归、多项式拟合或对特定函数的最小二乘法拟合）来评估观测到的气候指数期望值的趋势。这里的创新之处在于，从观测到的时间序列中减去期望值的趋势后，我们计算残差（异常值）的平方、立方、四次方以及任何其他组合的时间序列。然后我们将相同的标准趋势分析技术应用于这些新变量的时间序列。这种技术可用于确定观测到的气候是变得更具变化性还是更稳定。我们使用观测到的1901 - 2000年纽约市海平面变化、美国年平均降水量、美国修正的帕尔默干旱严重程度指数年平均值、全印度季风降雨指数以及南方涛动指数来说明该技术。在过去的100年中，这些气候指数的任何一个在变异性方面都没有显著的趋势。
We propose a new technique to analyze trends in moments of the statistical distribution of climatic indices. A standard approach (linear regression, polynomial fit, or least squares fit to a specific function) is first used to evaluate the trend in the expected value of an observed climatic index. The innovation here is that after the trend in the expected value is subtracted from the observed time series, we calculate the time series of the squares, cubes, fourth powers, and any other combination of the residuals (anomalies). Then we apply the same standard trend analysis technique to the time series of these new variables. This technique can be used to determine whether the observed climate is getting more or less variable. The observed 1901–2000 New York City sea level variations, U.S. annual average precipitation, U.S. annual averages of the Modified Palmer Drought Severity Index, All‐India Monsoon Rainfall Index, and Southern Oscillation Index are used to illustrate the technique. There are no significant trends in variability of any of these climatic indices for the past 100 years.