Testing ensembles of climate change scenarios for "statistical significance"

Climate impacts and adaptation research increasingly uses ensembles of regional and local climate change scenarios. To do so, the ensembles are examined to evaluate whether they describe a systematic difference between present states (and impacts) and envisaged future states-and such differences are often characterized as being statistically significant. This term "significance" is well defined by statistical terminology as the result of a test of a null hypothesis that is applied to samples of observations that are obtained with a defined sampling strategy. However such a statistical null hypothesis may not be a well-posed problem in the context of the evaluation of climate change scenarios. Therefore, the usage of terms such "statistically significant scenario" may be misunderstood in the general discourse about the certainty of projected climate change. We propose to employ instead a simple descriptive approach for characterizing the information in an ensemble of scenarios. Physical plausibility in the light of theoretical reasoning often adds robustness to the interpretation of climate change scenarios