Developing a likely climate scenario from multiple regional climate model simulations with an optimal weighting factor

摘要: This study presents a performance-based comprehensive weighting factor that accounts for the skill of different regional climate models (RCMs), including effect driving lateral boundary condition coming from either atmosphere–ocean global (AOGCMs) or reanalyses. A differential evolution algorithm is employed to identify optimal relative importance five performance metrics, and corresponding factors, include absolute mean error (RAME), annual cycle, spatial pattern, extremes multi-decadal trend. Based on cumulative density functions built by factors various RCMs/AOGCMs ensemble simulations, current future projections were then generated to identify level uncertainty in scenarios. selected areas southern Ontario Quebec Canada as case study. The main conclusions are follows: (1) Three metrics found essential, having greater importance: RAME, variability (2) choice conditions AOGCM had impacts factor, particularly winter season. (3) Combining based significantly increased consistency reduced spread among changes. These results imply play more important role reducing effects outliers plausible regions where there higher RCM/AOGCM simulations. As result weighting, substantial increases projected warming part area during summer, whole region winter, compared simple equal scheme RCM runs. an initial step toward developing likelihood procedure scenarios scale using probabilities all models.