Which significance test performs the best in climate simulations?
2014 (English)In: Tellus. Series A, Dynamic meteorology and oceanography, ISSN 0280-6495, E-ISSN 1600-0870, Vol. 66, 23139- p.Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
2014. Vol. 66, 23139- p.
autocorrelation, temporal correlation, internal variability, climate noise, significance test, Student's t-test
Meteorology and Atmospheric Sciences
IdentifiersURN: urn:nbn:se:su:diva-101493DOI: 10.3402/tellusa.v66.23139ISI: 000330634000001OAI: oai:DiVA.org:su-101493DiVA: diva2:704579