The Effect of Ensemble Weighting Scientific Achievement We

The Effect of Ensemble Weighting Scientific Achievement We explored the effect of weighting a regional climate model (RCM) ensemble using metrics developed for RCM performance evaluation. In most cases, when metrics are used for weights they do not improve ensemble mean bias or change ensemble mean projections. Additionally, we showed that any weighting scheme would often not be able to significantly change the mean projections regardless of weighting scheme. We also note that the universally-applicable RCM performance metrics used in the weighting scheme often did not significantly differentiate the models, and that the model Significance and Impact differentiation produced by the metrics did not always agree with an in -depth, process-level analysis. Significance and Impact Projections of (c, e) winter temperature and (d, f) precipitation with and without weights from 3 regions. Black curve represents the frequency of a given ensemble mean change using 1 000 different sets of random, uniformly distributed weights. The purple triangles indicate the 5 th and 95 th percentile values of change from this distribution. The dark orange line indicates the change from the unweighted ensemble mean; the dark teal line that from the ensemble mean weighted using the metrics without f 1; and the turquoise line that from the ensemble mean weighted using the metrics with f 1. The short orange lines indicate the individual simulation projections. The question of whether or not to weight a model ensemble often comes up when working with ensembles. We’ve proved that one should think twice before weighting, as it may not make a difference. Research Details Bukovsky, M. S. , J. Thompson, and L. O. Mearns, 2019: The effect of weighting on the NARCCAP ensemble mean. Does it make a difference? Can it make a difference? Clim. Res. , 77, 23 -43, doi: 10. 3354/cr 01541.