DOI: 10.1002/jgrd.50498
论文题名: Comparison of statistical estimates of potential seasonal predictability
作者: Feng X. ; Delsole T. ; Houser P.
刊名: Journal of Geophysical Research Atmospheres
ISSN: 21698996
出版年: 2013
卷: 118, 期: 12 起始页码: 6002
结束页码: 6016
语种: 英语
英文关键词: 2-m surface temperature
; AGCM
; Monte Carlo experiments
; potential seasonal predictability
; reanalysis
Scopus关键词: Atmospheric temperature
; Experiments
; Monte Carlo methods
; Regression analysis
; Spurious signal noise
; AGCM
; Monte Carlo experiments
; potential seasonal predictability
; Reanalysis
; Surface temperatures
; Estimation
; comparative study
; covariance analysis
; estimation method
; Monte Carlo analysis
; noise
; probability
; seasonal variation
; spatial analysis
; statistical analysis
; North America
英文摘要: Four methods for estimating potential seasonal predictability from a single time series are compared. The methods are: an analysis of variance procedure proposed by Shukla and Gutzler (SG), a spectral method proposed by Madden (MN), a bootstrap method proposed by the authors, and an analysis of covariance (ANOCOVA) method proposed by the authors. The time series used for comparison are taken from Monte Carlo simulations, an atmospheric general circulation model (AGCM), and reanalysis data. The comparison clearly reveals that SG systematically underestimates weather noise variance more strongly than the other methods and is therefore not a generally useful method. MN produces the least biased estimates of weather noise variance, but it tends to have a higher probability of identifying insignificant predictability than the other methods. Unfortunately, no simple, universally corrected statements can be made regarding the relative performances of MN, ANOCOVA, and bootstrap based on the AGCM output. Overall, the reanalysis-based estimates of potential predictability of seasonal mean temperature derived from these methods is generally in accord with previous estimates, both in spatial structure and in magnitude. Omitting SG, the other three methods consistently identify about 80% of the globe as significantly predictable, and about 5% of the globe as insignificantly predictable. The remaining 15% of the globe, mostly over extratropical land, yields inconsistent assessments of potential predictability, indicating sensitivity to the assumptions underlying each of the methods. Interestingly, winter mean temperature over most of North America is found to be insignificantly predictable by all three methods. Key Points Potential predictability is estimated using four statistical methods Comparisons use Monte Carlo experiments, AGCM data output and reanalysis data 80% or 5% of the globe are significantly or insignificantly predictable © 2013. American Geophysical Union. All Rights Reserved. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/63597
Appears in Collections: 影响、适应和脆弱性 气候减缓与适应
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作者单位: George Mason University, Fairfax, VA 22030, United States
Recommended Citation:
Feng X.,Delsole T.,Houser P.. Comparison of statistical estimates of potential seasonal predictability[J]. Journal of Geophysical Research Atmospheres,2013-01-01,118(12)