TITLE

Which significance test performs the best in climate simulations?

AUTHOR(S)
Decremer, Damien; Chung, Chul E.; Ekman, Annica M. L.; Brandefelt, Jenny
PUB. DATE
January 2014
SOURCE
Tellus: Series A;2014, Vol. 66, p1
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Climate change simulated with climate models needs a significance testing to establish the robustness of simulated climate change relative to model internal variability. Student's t-test has been the most popular significance testing technique despite more sophisticated techniques developed to address autocorrelation. We apply Student's t-test and four advanced techniques in establishing the significance of the average over 20 continuous-year simulations, and validate the performance of each technique using much longer (375-1000 yr) model simulations. We find that all the techniques tend to perform better in precipitation than in surface air temperature. A sizable performance gain using some of the advanced techniques is realised in the model Ts output portion with strong positive lag-1 yr autocorrelation (>+0.6), but this gain disappears in precipitation. Furthermore, strong positive lag-1 yr autocorrelation is found to be very uncommon in climate model outputs. Thus, there is no reason to replace Student's t-test by the advanced techniques in most cases.
ACCESSION #
95950031

 

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