Elshall Ahmed, Ye Ming, Kranz Sven A, Harrington Julie, Yang Xiaojuan, Wan Yongshan, Maltrud Mathew
Department of Earth, Ocean, and Atmospheric Science, Florida State University, Tallahassee, FL, USA.
Department of Bioengineering, Civil Engineering and Environmental Engineering, U. A. Whitaker College of Engineering, Florida Gulf Coast University, Fort Myers, FL, USA.
Clim Serv. 2022 Dec 1;28:1-13. doi: 10.1016/j.cliser.2022.100334.
Global climate models (GCMs) and Earth system models (ESMs) provide many climate services with environmental relevance. The High Resolution Model Inter-comparison 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.
全球气候模型（GCMs）和地球系统模型（ESMs）提供了许多与环境相关的气候服务。耦合模型比较计划第6阶段（CMIP6）的高分辨率模型相互比较项目（HighResMIP）提供了GCMs和ESMs的模型运行结果，以研究区域现象。开发一个简约的CMIP6集合需要多种集合方法，如独立模型子集选择、基于预筛选的子集选择和模型加权。本文介绍的工作重点是基于预筛选的子集选择的特定应用最优模型加权。因此，独立的集合成员根据其再现特定问题的物理可解释感兴趣特征的能力进行分类、选择和加权。我们通过对佛罗里达西海岸沿墨西哥湾赤潮预测的案例研究，讨论了最优模型加权的优点和注意事项。赤潮是全球范围内发生的特定有害藻华的俗称，会造成不利的社会经济和环境影响。我们的结果表明了基于预筛选的子集选择的重要性，因为最优模型加权可能会通过优化误差抵消而忽视稳健的集合成员。基于预筛选的子集选择还提供了有关模型权重有效性的见解。通过说明在使用最优模型加权时使用非代表性模型的注意事项，本研究的结果和讨论与许多其他气候服务相关。
