The observationally derived estimate of trend uncertainty is combined with the forced signal from LENS to produce an “Observational Large Ensemble” (OLENS).
The amplification of variability is greatest in the western United States and Alaska. The comparison suggests that uncertainty in trends due to internal variability is largely overestimated in LENS, which has an average amplification of variability of 32% across North America. This estimate is compared with the simulated trend uncertainty in the NCAR CESM1 Large Ensemble (LENS). Here, statistical resampling methods are applied to observations in order to quantify uncertainty in historical 50-yr (1966–2015) winter near-surface air temperature trends over North America related to incomplete sampling of internal variability. However, internal variability simulated by a model may be inconsistent with observations due to model biases. The contribution of internal variability to uncertainty in trends can be estimated in models as the spread across an initial condition ensemble. Accurate quantification of this uncertainty is critical for both contextualizing historical trends and determining the spread of climate projections. (c) The ratio of trend variability calculated from the 40 members of the NCAR CESM1 LENS to that inferred from applying the 2-yr block bootstrapping procedure to each member of LENS.Įstimates of the climate response to anthropogenic forcing contain irreducible uncertainty due to the presence of internal variability. (b) The ratio of trend variability calculated from 1000 50-yr segments of the NCAR CESM1 1800-yr control simulation to that inferred from applying the 2-yr block bootstrapping procedure to each segment. The black box outlines the typical range (☑ standard deviation around the mean) of autocorrelations and noise variances that we estimate for DJF temperatures across the observations and all members of the NCAR CESM1 Large Ensemble. (a) The ratio of trend variability calculated from synthetic AR(1) time series with specified variability and lag–1 year autocorrelations to that inferred from applying the 2-yr block bootstrapping procedure. Values close to unity indicate that the bootstrap is nearly unbiased. The magnitude of the values can be compared to those in Fig. In all subpanels, color indicates the ratio of the “true” standard deviation of trends due to internal variability to that inferred using block bootstrapping with a 2-yr block. Validation metrics for the bootstrapping methodology.
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