Credibility of Monthly Temperature Predictability Limit and Its Dependence on Length of Data
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摘要: 利用全国518个站1960—2011年逐日气温观测资料和160个站1983—2012年月尺度气温客观预测数据,基于非线性局部Lyapunov指数和非线性误差增长理论,研究中国区域月尺度气温可预报性期限对资料序列长度的依赖性。结果表明:气温可预报性期限对资料序列的长度有一定程度的依赖性,在西北、东北及华中地区尤为明显。平均而言,45年的资料序列长度才能够得到稳定合理的可预报性期限。为了验证气温可预报期限计算结果的可信度,将月尺度气温的可预报性期限与客观气候预测方法的预报评分技巧进行对比,发现两者结果非常一致。其中,由观测资料得到的1月气温的可预报性期限明显低于7月,1月客观气候预测方法的预报评分技巧也明显低于7月,且1月 (7月) 预报评分的空间分布型与1月 (7月) 气温可预报性期限的空间分布型较为一致。因此,利用非线性局部Lyapunov指数和台站逐日观测资料分析气温的可预报性期限结果是可信的。
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关键词:
- 月尺度气温;
- 非线性局部Lyapunov指数;
- 可预报性期限;
- 预报准确率
Abstract: Under the background of global warming, extreme temperature events in China are frequent in recent years, which cause serious influence on economic development and daily life of people. For the evolution of monthly temperature is influenced by initial forcing and boundary forcing, its variation is complex, and brings great challenge to climate prediction. A quantitative investigation is carried out on the monthly temperature predictability limit based on the nonlinear local Lyapunov exponent and daily temperature from 1960 to 2011 at 518 stations in China. But to get robust nonlinear local Lyapunov exponent, there should be enough observations. How much can the length of data series affect the robustness of monthly temperature predictability limit? And what about the credibility of monthly temperature predictability limit? These two questions need to be further analyzed.Based on the nonlinear local Lyapunov exponent and nonlinear error growth dynamics, quantitative analysis is carried out, and it shows that the robustness of monthly temperature predictability limit depends on the length of data series, especially in Northwest China, Northeast China and Central China. In western Inner Mongolia, south of the Yangtze River and South China, data series need to be more than 30 years long. On average, 45-year data series can ensure the stable monthly temperature predictability limit. The length of data series of 518 meteorological stations chosen in this study is 52 years, i.e., they all fit the basic need to evaluate monthly temperature predictability limit. To verify the credibility of monthly temperature predictability limit, the spatial pattern of monthly temperature predictability limit and two objective monthly temperature prediction results are compared. One method is persistent prediction, and the other is monthly dynamic extended range forecast based on climate models. It shows that the spatial distribution of monthly temperature predictability limit and prediction skill is very consistent. The monthly temperature predictability limit evaluated by observation in January is lower than that in July. Similarly, the prediction skill in January is also lower than that in July. What's more, the spatial pattern of objective climate prediction skill in January (July) is similar to the spatial pattern of monthly temperature predictability limit in the respective month. Thus, the monthly temperature predictability limit estimated by nonlinear local Lyapunov exponent and daily temperature from 1960 to 2011 at 518 stations is scientific and credible. And it provides important reference for improvement of monthly temperature prediction. -
图 5 中国区域1983—2012年观测气温与客观预报结果的时间相关系数
(粗线表示达到0.1显著性水平) (a) 与1月的月动力延伸预报,(b) 与7月的月动力延伸预报,(c) 与1月的持续性预报, (d) 与7月的持续性预报
Fig. 5 The temporal correlation coefficient between monthly observed temperature and monthly predicted temperature given by objective prediction methods in China from 1983 to 2012
(thick contour denotes passing the test of 0.1 level) (a) with monthly dynamic extended range forecast in January, (b) with monthly dynamic extended range forecast in July, (c) with persistent prediction in January, (d) with persistent prediction in July
图 6 1983—2012年客观预报对中国区域1月和7月平均气温的预报技巧
(a) 月动力延伸预报的预报评分,(b) 持续性预报的预报评分,(c) 月动力延伸预报的距平相关系数,(d) 持续性预报的距平相关系数 (曲线为5年平滑结果)
Fig. 6 The monthly temperature prediction skill of objective prediction methods in January and July from 1983 to 2012 in China
(a) prediction score of monthly dynamic extended range forecast, (b) prediction score of persistent prediction, (c) anomaly correlation coefficient of monthly dynamic extended range forecast, (d) anomaly correlation coefficient of persistent prediction (solid line denotes five-year running mean series)
表 1 月动力延伸预报和持续性预报对月平均温度的预测在1983—2012年的评估中1月和7月占优的站数比率 (单位:%)
Table 1 The percentage of stations in which the monthly temperature prediction skill of monthly dynamic extended range forecast or persistent prediction is higher in January and July from 1983 to 2012(unit:%)
客观预报方法 预报评分 距平相关系数 1月 7月 1月 7月 月动力延伸预报 9 91 4 96 持续性预报 30 70 24 76 表 2 月动力延伸预报和持续性预报对月平均气温预测1983—2012年评估中1月和7月占优的年数比率 (单位:%)
Table 2 The percentage of years in which the monthly temperature prediction skill of monthly dynamic extended range forecast or persistent prediction is higher in January and July from 1983 to 2012(unit:%)
客观预报方法 预报评分 距平相关系数 1月 7月 1月 7月 月动力延伸预报 30 70 23 77 持续性预报 37 63 37 63 -
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