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A skill assessment framework for the Fisheries and Marine Ecosystem Model Intercomparison Project
  • +13
  • Nina Rynne,
  • Camilla Novaglio,
  • Julia L. Blanchard,
  • Daniele Bianchi,
  • Villy Christensen,
  • Marta Coll,
  • Jerome Guiet,
  • Jeroen Gerhard Steenbeek,
  • Andrea Bryndum-Buchholz,
  • Tyler Eddy,
  • Cheryl Shannon Harrison,
  • Olivier Maury,
  • Kelly Ortega-Cisneros,
  • Colleen M Petrik,
  • Derek Tittensor,
  • Ryan Heneghan
Nina Rynne
Queensland University of Technology

Corresponding Author:[email protected]

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Camilla Novaglio
University of Tasmania
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Julia L. Blanchard
University of Tasmania
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Daniele Bianchi
University of California Los Angeles
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Villy Christensen
UBC Insitute of the Oceans and Fisheries
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Marta Coll
Institute of Marine Science (ICM-CSIC), Barcelona, Spain
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Jerome Guiet
University of California, Los Angeles
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Jeroen Gerhard Steenbeek
Ecopath International Initiative (EII)
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Andrea Bryndum-Buchholz
Centre for Fisheries Ecosystems Research, Fisheries and Marine Institute, Memorial University of Newfoundland
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Tyler Eddy
Memorial University
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Cheryl Shannon Harrison
Louisiana State University
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Olivier Maury
Institut de Recherche pour le Développement
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Kelly Ortega-Cisneros
Department of Biological Sciences, University of Cape Town, Cape Town, South Africa
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Colleen M Petrik
UC San Diego
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Derek Tittensor
Department of Biology, Dalhousie University, 1355 Oxford Street, Halifax, NS B3H 4R2, Canada
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Ryan Heneghan
School of Science and Environment
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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 modelling 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 characterise 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.
14 May 2024Submitted to ESS Open Archive
15 May 2024Published in ESS Open Archive