Table 1.
List of CMIP5 members.
Table 2.
List of CMIP6 members.
Fig 1.
Time series of SAT and SLP based climate indices (see text for details). a) AMO; b) PMO; c) NMO; d) NAO; and e) ALPI. Red curves show raw annual ensemble-mean time series from the 20CRv3 reanalysis. Light gray curves show rescaled forced-signal estimates from the 17 CMIP5 models considered; their average is shown in black.
Fig 2.
M-SSA Spectra of Internal Variability.
M-SSA spectra of internal variability — that is, variances of M-SSA modes ranked in the order of decreasing variance — from (a, b) 20CRv3 and (c, d) ERA-20C reanalysis (blue) and (a, c) CMIP5 and (b, d) nonoutlier CMIP6 simulations (red). Green bars show variances associated with projections of CMIP5 simulated internal variability onto the 20CRv3-based space-time EOFs. Error bars represent the 25th and 75th percentiles about the ensemble mean for each ensemble of spectral estimates shown.
Fig 3.
M-SSA Spectra of Internal Variability for Outlier Runs.
Same as Fig 2, but for CMIP6 outlier runs. Observed internal variability is still based on all 21 forced signal estimates from CMIP6 members, not solely the outlier models.
Fig 4.
Observed reconstructed components for a) 20CRv3 (based on rescaled CMIP5 forced signals); b) ERA20c (based on rescaled CMIP5 forced signals); c) 20CRv3 (based on rescaled CMIP6 forced signals); and d) ERA20c (based on rescaled CMIP6 forced signals). For visual convenience, the indices are normalized to unit standard deviation and offset by 2 in the vertical to avoid overlap and the NAO and ALPI indices have been inverted. The error bars show the standard spread of the multidecadal signal across its entire set of estimates associated with all available combinations of multiple reanalysis samples and CMIP estimated forced signals.
Fig 5.
Taylor Diagrams in Climate Index Space.
Taylor diagrams comparing the observed (crosses) and model simulated (circles) multidecadal patterns in the effective space of climate indices. Taylor diagrams are plotted in polar coordinates. The distance from the origin indicates the pattern’s standard deviation (denoted by the dotted arches radiating away from the origin, labeled by the axes) and the cosine of the angle between the diagrams’ horizontal axis and a ray emanating from the origin and passing through the point associated with this pattern gives the value of the spatial correlation between this pattern and the reference pattern (these dotted rays emanating from the origin are labeled with magenta numbers, spanning from a correlation of 0 to 1). The reference pattern (positioned at (0,1) is denoted by the large dot. The distance of each estimate or model run from the reference pattern measures the dissimilarity between the two (RMSE, denoted by the half circles radiating from the reference pattern dot). The results are shown for: CMIP5 runs in the first row (a, b); CMIP6 nonoutliers in the second row (c, d); 20CRv3 in the first column (a, c); and ERA20c in the second column (b, d). The reference patterns are computed as the ensemble-mean reanalysis patterns in each case.
Fig 6.
Outlier Models Taylor Diagrams in Climate Index Space.
Same as in Fig 5, but only including CMIP6 outlier runs.
Fig 7.
Multi-model Ensemble-Mean Taylor Diagram.
Same as in Fig 5, but with the multi-model ensemble-mean reference pattern.
Fig 8.
Outlier Models Multi-Model Ensemble-Mean Taylor Diagram.
Same as in Fig 6 (for outlier runs), but with the outlier runs’ multi-model ensemble mean as the reference pattern.
Fig 9.
SAT Multidecadal Physical Patterns.
Regression of SAT (°C) on the leading PC of five M-SSA filtered climate indices of SAT (°C). (Please note that, despite not being the leading EOF, they are referred to as such in the figure titles for brevity). The EOF patterns were computed by regressing the time series of the estimated SAT internal variability at each grid node onto the normalized leading PC of the corresponding M-SSA filtered set of the internal variability in the five climate indices considered (section 2.4). Color shading represents the ensemble-mean EOF pattern based on (a) 20CRv3; (b) ERA-20C; (c) CMIP5; and (d) nonoutlier CMIP6 based internal variability estimates. In (a), stippling indicates grid nodes where at least 95% (out of 80x17=1360) of the 20CRv3 ensemble members have the pattern of the same sign; in (b) stippling marks the regions over which at least 13 out of 17 ERA20c based patterns have the same sign at a given grid node, which corresponds to the 95th percentile of the binomial distribution with p=0.5 and N=17, in (c, d) stippling has been changed to represent grid nodes where at least 80% of the ensemble of patterns have the same sign. Coastline data were obtained and plotted using The MathWorks, Inc.’s Mapping Toolbox.
Fig 10.
SLP Multidecadal Physical Patterns.
Same as Fig 9, but for SLP. Coastline data were obtained and plotted using The MathWorks, Inc.’s Mapping Toolbox.
Table 3.
Percentiles of spatial correlations between MMM and ensemble-mean reanalysis SLP patterns.
Fig 11.
Outlier Models Multidecadal Physical Patterns.
Same as Fig 9 and Fig 10, but for SAT and SLP from the outlier models. Coastline data were obtained and plotted using The MathWorks, Inc.’s Mapping Toolbox.
Fig 12.
Taylor Diagrams for SAT Patterns.
Taylor diagrams for the Northern Hemisphere multidecadal SAT patterns’ comparisons to the (ensemble-mean) reanalysis reference pattern (analogous to climate-index pattern analysis of Fig 5).
Fig 13.
Outlier Models Taylor Diagrams for SAT Patterns.
Same as Fig 12, but for the CMIP6 outlier models and with the AMO patterns that display higher multidecadal variability.
Fig 14.
Taylor Diagrams for SLP Patterns.
Same as Fig 12, but for the Northern Hemisphere SLP internal variability patterns.
Fig 15.
Multi-model Ensemble-mean Taylor Diagram for SLP Patterns.
Same as Fig 14, but with the multi-model ensemble-mean SLP pattern as the reference pattern.