Application-specific optimal model weighting of global climate models: A red tide example
Elshall, A., M. Ye, S. Kranz, X. Yang, Y. Wan, AND M. Maltrud. Application-specific optimal model weighting of global climate models: A red tide example. Climate Services. Elsevier B.V., Amsterdam, Netherlands, 28:100334, (2022). https://doi.org/10.1016/j.cliser.2022.100334
This study discusses the application-specific optimal model weighting of Earth System Models (ESMs) to improve predictive performance of red tide in the Gulf of Mexico. Including non-representative models can underplay the model weights of robust models while excluding all non-representative models results in the most parsimonious ensemble accounting for both ensemble size and performance. Prescreening-based subset selection screens and selects ensemble members based on their ability to reproduce certain key features. This study concludes that prescreening-based subset selection is a viable option that can either substitute model weighting, or be used prior to model weighing, to improve model performance. This will be an essential addition to the literature of application-specific optimal model weighting of ESMs.
Global climate models (GCMs) and Earth system models (ESMs) provide many climate services with environmental relevance. The High Resolution Model Intercomparison Project (HighResMIP) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) provides model runs of GCMs and ESMs to address regional phenomena. Developing a parsimonious ensemble of CMIP6 requires multiple ensemble methods such as independent-model subset selection, prescreening-based subset selection, and model weighting. The work presented here focuses on application-specific optimal model weighting, with prescreening-based subset selection. As such, independent ensemble members are categorized, selected, and weighted based on their ability to reproduce physically-interpretable features of interest that are problem-specific. We discuss the strengths and caveats of optimal model weighting using a case study of red tide prediction in the Gulf of Mexico along the West Florida Shelf. Red tide is a common name of specific harmful algal blooms that occur worldwide, causing adverse socioeconomic and environmental impacts. Our results indicate the importance of prescreening-based subset selection as optimal model weighting can underplay robust ensemble members by optimizing error cancellation. Prescreening-based subset selection also provides insights about the validity of the model weights. By illustrating the caveats of using non-representative models when optimal model weighting is used, the findings and discussion of this study are pertinent to many other climate services.