globalchange  > 气候变化事实与影响
DOI: 10.1175/JCLI-D-14-00668.1
Scopus记录号: 2-s2.0-84950108824
论文题名:
The effects of gridding algorithms on the statistical moments and their trends of daily surface air temperature
作者: Cavanaugh N.R.; Shen S.S.P.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2015
卷: 28, 期:23
起始页码: 9188
结束页码: 9205
语种: 英语
Scopus关键词: Algorithms ; Atmospheric temperature ; Climate models ; Climatology ; Probability density function ; Temperature ; Anomalies ; Climate variability ; Interpolation schemes ; Statistical techniques ; Surface observation ; Trends ; Variability ; Climate change ; air temperature ; algorithm ; climate variation ; interpolation ; numerical model ; probability density function ; regional climate ; surface temperature ; temperature profile
英文摘要: This paper explores the effects from averaging weather station data onto a grid on the first four statistical moments of daily minimum and maximum surface air temperature (SAT) anomalies over the entire globe. The Global Historical Climatology Network-Daily (GHCND) and the Met Office Hadley Centre GHCND (HadGHCND) datasets from 1950 to 2010 are examined. The GHCND station data exhibit large spatial patterns for each moment and statistically significant moment trends from 1950 to 2010, indicating that SAT probability density functions are non-Gaussian and have undergone characteristic changes in shape due to decadal variability and/or climate change. Comparisons with station data show that gridded averages always underestimate observed variability, particularly in the extremes, and have altered moment trends that are in some cases opposite in sign over large geographic areas. A statistical closure approach based on the quasi-normal approximation is taken to explore SAT's higher-order moments and point correlation structure. This study focuses specifically on relating variability calculated from station data to that from gridded data through the moment equations for weighted sums of random variables. The higher-order and nonlinear spatial correlations up to the fourth order demonstrate that higher-order moments at grid scale can be determined approximately by functions of station pair correlations that tend to follow the usual Kolmogorov scaling relation. These results can aid in the development of constraints to reduce uncertainties in climate models and have implications for studies of atmospheric variability, extremes, and climate change using gridded observations. © 2015 American Meteorological Society.
资助项目: NSF, National Science Foundation ; NSF, National Science Foundation ; NSF, National Science Foundation ; NSF, National Science Foundation ; NSF, National Science Foundation
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/50584
Appears in Collections:气候变化事实与影响

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作者单位: Earth Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States; Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, United States; Department of Mathematics and Statistics, San Diego State University, San Diego, CA, United States

Recommended Citation:
Cavanaugh N.R.,Shen S.S.P.. The effects of gridding algorithms on the statistical moments and their trends of daily surface air temperature[J]. Journal of Climate,2015-01-01,28(23)
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