Uncertainty components estimates in transient climate projections. Bias of moment-based estimators in the single time and time series approaches.

Abstract : In most climate impact studies, model uncertainty in projections is estimated as the empirical variance of the climate responses for the different modeling chains. These estimates are however biased. We explore the importance of the bias for a simple but classical configuration where uncertainties in projections are composed of two sources: model uncertainty and internal climate variability. We derive exact formulation of the bias. It is positive, i.e. the empirical variance tends to overestimate the true model uncertainty variance. It can be especially high when a single time ANOVA is used for the analysis. In the most critical configurations, when the number of members available for each modeling chain is small (≤ 3) and when internal variability explains most of total uncertainty variance (75% or more), the overestimation is higher than 100% of the true model uncertainty variance. The bias is considerably smaller with a time series ANOVA approach, owing to the multiple time steps accounted for. The longer the transient time period used for the analysis, the smaller the bias. When a quasi-ergodic ANOVA approach is applied to decadal data for the whole 1980-2100 period, the bias is up to 2.5 to 20 times smaller than that obtained with a single time approach, depending on the projection lead time. Whatever the approach, the bias is likely to be not negligible for a large number of climate impact studies resulting in a likely large overestimation of the contribution of model uncertainty to total variance. In many cases, classical empirical estimators of model uncertainty should be thus biascorrected.
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[Research Report] Université grenoble Alpes, CNRS, Institut des Géosciences et de l'Environnement. 2018
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Benoit Hingray, Juliette Blanchet. Uncertainty components estimates in transient climate projections. Bias of moment-based estimators in the single time and time series approaches.. [Research Report] Université grenoble Alpes, CNRS, Institut des Géosciences et de l'Environnement. 2018. 〈hal-01738218〉

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