Abstract

We explore the effect of weighting using the performance metrics developed for weighting in the ENSEMBLES program on the regional climate model (RCM) ensemble produced as a part of the North American Regional Climate Change Assessment Program (NARCCAP). We consider weighting a reanalysis-driven e... Show moreWe explore the effect of weighting using the performance metrics developed for weighting in the ENSEMBLES program on the regional climate model (RCM) ensemble produced as a part of the North American Regional Climate Change Assessment Program (NARCCAP). We consider weighting a reanalysis-driven ensemble, as well as the effect on baseline GCM-driven ensemble mean bias, and mean climate change projections. This work evaluates when, where, and how weighting affects the ensemble mean results. We conclude that, in most cases, the metrics and resulting weights do not substantially differentiate the simulations. Also, the metrics do not always produce the same quality ranking results as an in-depth process-level analysis, implying that important, region-specific processes may be missing from this more universally applicable set of metrics. Moreover, it is found that when the metrics are used as weights, they do not consistently improve ensemble mean bias, as was found in the ENSEMBLES program. Furthermore, this analysis notably finds that weighting does not substantially change mean climate change projections unless all weight is applied to one or a few of the simulations that sit towards the extremes of the ensemble distribution, nor can it. We demonstrate why this is so. Show less