Which significance test performs the best in climate simulations?

• 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.

Subject headings

NATURVETENSKAP  -- Geovetenskap och miljövetenskap -- Meteorologi och atmosfärforskning (hsv//swe)

NATURAL SCIENCES  -- Earth and Related Environmental Sciences -- Meteorology and Atmospheric Sciences (hsv//eng)

Keyword

autocorrelation

temporal correlation

internal variability

climate noise

significance test

Student's t-test

Publication and Content Type

ref (subject category)

art (subject category)