Application-specific optimal model weighting of global climate models: A red tide example
Citation:
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
Impact/Purpose:
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.
Description:
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.
URLs/Downloads:
DOI: Application-specific optimal model weighting of global climate models: A red tide example