![]() Ensemble methods aim at selecting and combining multiple ESMs to form a robust and diverse ensemble of models. Multi-model ensemble refers to an ensemble of multiple ESMs with single or multiple realizations of each ESM. Single model ensemble is a single Earth system model (ESM) with multiple realizations given perturbed parameters, initialization, physics, and forcings. Ensemble methods are an active research area as multi-model ensemble can be more robust then a single-model ensemble ( DelSole et al., 2014 Al Samouly et al., 2018 Wallach et al., 2018). ![]() et al., 2016 Gutowski et al., 2020), and ensemble methods to select and combine different models. ![]() To improve raw outputs directly given by Earth system models (ESMs) for providing useful services to societal decision making, a combination of multiple methods is often used such as bias-correction to account for systematic errors ( Szabó-Takács et al., 2019 Wang et al., 2019), ensemble recalibration to improve ensemble characteristics ( Manzanas et al., 2019), downscaling to improve the spatial and temporal resolution ( Gutowski Jr. Moreover, this research serves as a starting point to build upon for red tide management, using the publicly available CMIP, Coordinated Regional Downscaling Experiment (CORDEX), and reanalysis data. This allows for efficient and transparent testing of the results’ sensitivity to different modeling assumptions. As such, the interactive Colab notebooks developed for data analysis are annotated in the paper. Additionally, our analysis follows the FAIR Guiding Principles for scientific data management and stewardship such that data and analysis tools are findable, accessible, interoperable, and reusable. These findings are pertinent to other regional environmental management applications and climate services. The study results highlight the importance of prescreening-based subset selection with decision relevant metrics in identifying non-representative models, understanding their impact on ensemble prediction, and improving the ensemble prediction. We present ensemble method for improving red tide prediction using the high resolution ESMs of the Coupled Model Intercomparison Project Phase 6 (CMIP6) and reanalysis data. Red tide is a common name for harmful algal blooms that occur worldwide, which result from large concentrations of aquatic microorganisms, such as dinoflagellate Karenia brevis, a toxic single celled protist. We apply the method to improve the prediction of red tide along the West Florida Shelf in the Gulf of Mexico, which affects coastal water quality and has substantial environmental and socioeconomic impacts on the State of Florida. The ensemble size is then updated by selecting the subsets that improve the performance of the ensemble prediction using decision relevant metrics. ![]() In the prescreening step, the independent ensemble members are categorized based on their ability to reproduce physically-interpretable features of interest that are regional and problem-specific. We present the ensemble method of prescreening-based subset selection to improve ensemble predictions of Earth system models (ESMs). ![]() 5Fluid Dynamics and Solid Mechanics Group, Los Alamos National Laboratory, Los Alamos, NM, United States.4Center for Environmental Measurement and Modeling, United States Environmental Protection Agency, Gulf Breeze, FL, United States.3Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, United States.2Center for Economic Forecasting and Analysis, Florida State University, Tallahassee, FL, United States.1Department of Earth, Ocean, and Atmospheric Science, Florida State University, Tallahassee, FL, United States.Kranz 1, Julie Harrington 2, Xiaojuan Yang 3, Yongshan Wan 4 and Mathew Maltrud 5 ![]()
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