Credibility of Monthly Temperature Predictability Limit and Its Dependence on Length of Data

Liu Jingpeng; Chen Lijuan; Li Weijing; Zhang Peiqun; Zuo Jinqing

doi:10.11898/1001-7313.20150203

Vol.26, NO.2, 2015

Turn off MathJax

Article Contents

Article Navigation > Journal of Applied Meteorological Science > 2015 > 26(2): 151-159

Liu Jingpeng, Chen Lijuan, Li Weijing, et al. Credibility of monthly temperature predictability limit and its dependence on length of data. J Appl Meteor Sci, 2015, 26(2): 151-159. DOI: 10.11898/1001-7313.20150203.

Citation:

Liu Jingpeng, Chen Lijuan, Li Weijing, et al. Credibility of monthly temperature predictability limit and its dependence on length of data. J Appl Meteor Sci, 2015, 26(2): 151-159. DOI: 10.11898/1001-7313.20150203.

Citation:

PDF( 4031 KB)

Credibility of Monthly Temperature Predictability Limit and Its Dependence on Length of Data

DOI: 10.11898/1001-7313.20150203

1.
Chinese Academy of Meteorological Sciences, Beijing 100081
2.
University of Chinese Academy of Sciences, Beijing 100049
3.
Laboratory of Climate Studies, National Climate Center, CMA, Beijing 100081

Received Date: 2014-12-08
Rev Recd Date: 2014-12-29
Publish Date: 2015-03-31

Abstract

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.
- monthly temperature;
- nonlinear local Lyapunov exponent;
- predictability limit;
- prediction skill

FullText(HTML)

References(26)

Figures and Tables

Fig. 1 Monthly temperature predictability limit under different lengths of data at Minqin Station of Gansu Province

DownLoad: Full-Size Img PowerPoint

Fig. 2 The shortest length of data needed to get the robust estimation of monthly temperature predictability limit (a) and the standard deviation of intraseasonal temperature data series (b)

DownLoad: Full-Size Img PowerPoint

Fig. 3 The difference between the monthly temperature predictability limit in January and July (unit:d)

DownLoad: Full-Size Img PowerPoint

Fig. 4 The prediction score of persistent temperature prediction in January (a) and July (b) from 1983 to 2012 in China

DownLoad: Full-Size Img PowerPoint

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

DownLoad: Full-Size Img PowerPoint

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)

DownLoad: Full-Size Img PowerPoint

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
客观预报方法	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

DownLoad: Download CSV

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
客观预报方法	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

DownLoad: Download CSV

References

[1]	Zhai P, Pan X.Trends in temperature extremes during 1951-1999 in China.Geophys Res Lett, 2003, 30(17):1913. http://adsabs.harvard.edu/abs/2003GeoRL..30.1913Z
[2]	Gao X J, Zhao Z C, Filippo G.Changes of extreme events in regional climate simulations over East Asia.Adv Atmos Sci, 2002, 19(5):927-942. doi: 10.1007/s00376-002-0056-2
[3]	杨萍, 刘伟东, 王启光, 等.近40年我国极端温度变化趋势和季节特征.应用气象学报, 2010, 21(1):29-36. doi: 10.11898/1001-7313.20100104
[4]	丁一汇, 王遵娅, 宋亚芳, 等.中国南方2008年1月罕见低温雨雪冰冻灾害发生的原因及其与气候变暖的关系.气象学报, 2008, 66(5):808-825. doi: 10.11676/qxxb2008.074
[5]	林玉成, 徐珺, 张芳华.2013年7月大气环流和天气分析.气象, 2013, 39(10):1379-1384. doi: 10.7519/j.issn.1000-0526.2013.10.018
[6]	杨舒楠, 何立富.2013年8月大气环流和天气分析.气象, 2013, 39(11):1521-1528. doi: 10.7519/j.issn.1000-0526.2013.11.017
[7]	贾小龙, 陈丽娟, 高辉, 等.我国短期气候预测技术进展.应用气象学报, 2013, 24(6):641-655. doi: 10.11898/1001-7313.20130601
[8]	王会军, 陈丽娟, 李维京, 等.中国区域月平均温度和降水的模式可预报性分析.气象学报, 2007, 65(5):725-732. doi: 10.11676/qxxb2007.068
[9]	陈丽娟, 李维京, 刘绿柳, 等.中国区域月气候预测方法和预测能力评估.高原气象, 2008, 27(4):838-843. http://www.cnki.com.cn/Article/CJFDTOTAL-GYQX200804018.htm
[10]	Reichler T, Roads J.The role of boundary and initial conditions for dynamical seasonal predictability.Nonlinear Proc Geoph, 2003, 10(3):211-232. doi: 10.5194/npg-10-211-2003
[11]	Lorenz E N.Deterministic nonperiodic flow.J Atmos Sci, 1963, 20(2):130-141. doi: 10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2
[12]	Lorenz E N.Atmospheric predictability as revealed by naturally occurring analogues.J Atmos Sci, 1969, 26(4):636-646. doi: 10.1175/1520-0469(1969)26<636:APARBN>2.0.CO;2
[13]	Reichler T, Roads J O.Time-space distribution of long-range atmospheric predictability.J Atmos Sci, 2004, 61(3):249-263. doi: 10.1175/1520-0469(2004)061<0249:TDOLAP>2.0.CO;2
[14]	Shukla J.Predictability in the midst of chaos:A scientific basis for climate forecasting.Science, 1998, 282(5389):728-731. doi: 10.1126/science.282.5389.728
[15]	范晓青, 李维京.模式大气月尺度可预报性的对比研究.应用气象学报, 2003, 14(1):49-60. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20030106&flag=1
[16]	李建平, 丁瑞强.短期气候可预报期限的时空分布.大气科学, 2008, 32(4):975-986. http://www.cnki.com.cn/Article/CJFDTOTAL-DQXK200804021.htm
[17]	Yang X Q, Anderson J L, Stern W F.Reproducible forced modes in AGCM ensemble integrations and potential predictability of atmospheric seasonal variations in the extratropics.J Climate, 1998, 11(11):2942-2959. doi: 10.1175/1520-0442(1998)011<2942:RFMIAE>2.0.CO;2
[18]	乐群, 曹俊武.中国月平均温度的气候噪声和潜在可预报性.气象学报, 1999, 57(5):605-612. http://www.cnki.com.cn/Article/CJFDTOTAL-QXXB199905009.htm
[19]	陈宝花, 李建平, 丁瑞强, 等.非线性局部Lyapunov指数与大气可预报性研究.中国科学D辑:地球科学, 2006, 36(11):1068-1076. http://www.cnki.com.cn/Article/CJFDTOTAL-JDXK200611009.htm
[20]	Li J, Ding R.Temporal-spatial distribution of atmospheric predictability limit by local dynamical analogs.Mon Wea Rev, 2011, 139(10):3265-3283. doi: 10.1175/MWR-D-10-05020.1
[21]	李维京, 刘景鹏, 陈丽娟, 等.中国月平均气温可预报性的时空特征及其年代际变化.科学通报, 2014, 59(25):2520-2527. http://www.cnki.com.cn/Article/CJFDTOTAL-KXTB201425010.htm
[22]	杨培才, 陈烈庭.埃尔尼诺/南方涛动的可预报性.大气科学, 1990, 14(1):64-71. http://www.cnki.com.cn/Article/CJFDTOTAL-DQXK199001008.htm
[23]	李维京, 张培群, 李清泉, 等.动力气候模式预测系统业务化及其应用.应用气象学报, 2005, 16(3):1-11. http://www.cnki.com.cn/Article/CJFDTOTAL-YYQX2005S1000.htm
[24]	姚菊香, 王盘兴, 李丽平.季节内振荡研究中两种数字滤波器的性能对比.南京气象学院学报, 2005, 28(2):248-253. http://www.cnki.com.cn/Article/CJFDTOTAL-NJQX20050200D.htm
[25]	纪忠萍, 谷德军, 吴乃庚, 等.广东省前汛期暴雨与500 hPa关键区准双周振荡.应用气象学报, 2010, 21(6):671-684. doi: 10.11898/1001-7313.20100604
[26]	陈桂英, 赵振国.短期气候预测评估方法和业务初估.应用气象学报, 1998, 9(2):178-185. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=19980225&flag=1