期刊
TELLUS SERIES A-DYNAMIC METEOROLOGY AND OCEANOGRAPHY
卷 66, 期 -, 页码 -出版社
CO-ACTION PUBLISHING
DOI: 10.3402/tellusa.v66.23139
关键词
autocorrelation; temporal correlation; internal variability; climate noise; significance test; Student's t-test
资金
- Korea Meteorological Administration Research and Development Program [CATER 2012-7100]
- Korean-Sweden Research Cooperation Program of the National Research Foundation of Korea [2012K2A3A1035889]
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.
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