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A Skill Assessment Framework for the Fisheries and Marine Ecosystem Model Intercomparison Project
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A Skill Assessment Framework for the Fisheries and Marine Ecosystem Model Intercomparison Project
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A Skill Assessment Framework for the Fisheries and Marine Ecosystem Model Intercomparison Project
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Journal Article
A Skill Assessment Framework for the Fisheries and Marine Ecosystem Model Intercomparison Project
2025
Overview
Understanding climate change impacts on global marine ecosystems and fisheries requires complex marine ecosystem models, forced by global climate projections, that can robustly detect and project changes. The Fisheries and Marine Ecosystems Model Intercomparison Project (FishMIP) uses an ensemble modeling approach to fill this crucial gap. Yet FishMIP does not have a standardised skill assessment framework to quantify the ability of member models to reproduce past observations and to guide model improvement. In this study, we apply a comprehensive model skill assessment framework to a subset of global FishMIP models that produce historical fisheries catches. We consider a suite of metrics and assess their utility in illustrating the models' ability to reproduce observed fisheries catches. Our findings reveal improvement in model performance at both global and regional (Large Marine Ecosystem) scales from the Coupled Model Intercomparison Project Phase 5 and 6 simulation rounds. Our analysis underscores the importance of employing easily interpretable, relative skill metrics to estimate the capability of models to capture temporal variations, alongside absolute error measures to characterize shifts in the magnitude of these variations between models and across simulation rounds. The skill assessment framework developed and tested here provides a first objective assessment and a baseline of the FishMIP ensemble's skill in reproducing historical catch at the global and regional scale. This assessment can be further improved and systematically applied to test the reliability of FishMIP models across the whole model ensemble from future simulation rounds and include more variables like fish biomass or production. Plain Language Summary To understand how climate change affects the world's oceans and fisheries, scientists use complex models that predict changes based on climate data. One specific initiative, the Fisheries and Marine Ecosystems Model Intercomparison Project (FishMIP), employs a variety of these models together to enhance predictions. However, FishMIP lacks a standardized method to evaluate how well these models match past data and to identify areas for improvement. In this study, we developed and applied a detailed evaluation method to some FishMIP models that predict historical fish catches. We used different measures to determine how accurately these models can replicate actual fish catches. Our results showed that the models have improved at predicting fish catches, both globally and in specific large marine regions, through two rounds of model improvements. We emphasized the value of using clear and relative measures for understanding the models' accuracy over time and specific measures to identify the differences in predictions between models and over time. This evaluation provides a baseline for understanding how well FishMIP can reproduce past fishing data and suggests ways to further refine and test these models in future studies, potentially including additional factors like fish numbers or overall productivity. Key Points We developed a standardised skill assessment framework for an ensemble of global marine ecosystem models Selected models show agreement with the trajectory of fisheries catch, but exhibit biases compared to observed absolute catch values Our framework provides a solid basis to guide global marine ensemble model improvement and increase credibility of ensemble projections
